Workflows/Build a coding assistant with Strata (Qwen3.8-Flash-Next Coder)
Coding / WORKFLOW
Build a coding assistant with Strata (Qwen3.8-Flash-Next Coder)
Run Qwen3.8-Flash-Next through Strata, connect VS Code and Cline, and complete a real coding task. Choose the smaller Coder derivative or a full pack, then follow Windows, Linux or RunPod instructions. Compare actual speed, memory use, and tool results on your own setup.
Partially field-testedSource checked 10/10/2026
LOCALRENTED GPU
Freshness 82 out of 100. Repository age: 52 days old, Aug 24, 2026. Measures age, not quality.82/ 100Freshness52 days oldRepository age · Aug 24, 2026
Popularity 88 out of 100. 2,290,500 downloads in 30 days; 6,235 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.88/ 100Popularity2.3M downloads / 30d6.2K lifetime likes · Checked Oct 15, 2026
What do these scores mean?+
Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.
Freshness · release age
A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.
Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method →
USER-REPORTED FIELD OBSERVATIONPARTIAL DEPLOYMENT
Model loaded. Remote chat and coding still need validation.
Coder IQ1_M reportedly loaded, answered a local /health request, and displayed Chat / Monitor / About through Jupyter. Submitting browser chat then returned a Strata host/origin 403. The prescribed origin fix, a successful SSH tunnel, and a complete Cline task have not been confirmed.
Model + local healthReported successful
Jupyter dashboardReported accessible
Browser chat + ClineValidation incomplete
October 9, 2026 · Reported by the user in supplied installation notes. YouRunAI has not independently reproduced this deployment or completed the remaining tests.
GPU memory and system RAM are separate. These are documented capacity recommendations, not speed tests. Coder loaded on the user-reported Pod; 31 GB is below the 32 GB RAM recommendation. Remote chat and Cline remain unconfirmed.
Conditional; verify supported card, backend, and RAM allocation.
Conditional; verify supported card, backend, and RAM allocation.
Conditional; verify supported card, backend, and RAM allocation.
Conditional; verify supported card, backend, and RAM allocation.
Conditional; verify supported card, backend, and RAM allocation.
Intel Arc GPU · 16 GB VRAM / 64 GB RAM
Experimental Linux source build only; use a supported NVIDIA/AMD or remote setup here.
Experimental Linux source build only; use a supported NVIDIA/AMD or remote setup here.
Experimental Linux source build only; use a supported NVIDIA/AMD or remote setup here.
Experimental Linux source build only; use a supported NVIDIA/AMD or remote setup here.
Experimental Linux source build only; use a supported NVIDIA/AMD or remote setup here.
Apple Silicon · 64 GB unified memory
No official native Strata route; use Mac as a remote Cline client.
No official native Strata route; use Mac as a remote Cline client.
No official native Strata route; use Mac as a remote Cline client.
No official native Strata route; use Mac as a remote Cline client.
No official native Strata route; use Mac as a remote Cline client.
CPU only · 64 GB system RAM
No supported CPU-only setup in this guide; use a supported GPU server.
No supported CPU-only setup in this guide; use a supported GPU server.
No supported CPU-only setup in this guide; use a supported GPU server.
No supported CPU-only setup in this guide; use a supported GPU server.
No supported CPU-only setup in this guide; use a supported GPU server.
8 GB VRAM is experimental and slow in upstream docs. No token rate is promised here. Coder needs about 64 GB of downloaded pack/draft files; allow 120 GB working space, or 150 GB for a full pack.
Context window
Context values are conservative starting settings. They must match both the server and Cline. More context can reduce GPU expert-cache space and increase first-token wait.
Machine
Coder
Full Q2_0
Full IQ2_XS
Full IQ3_XXS
Full IQ3_S
NVIDIA or AMD · 12–16 GB GPU / 32 GB RAM
Start at 16K; reduce to 8K if memory is tight.
Choose 48–64 GB RAM first.
Choose 48–64 GB RAM first.
Choose 64 GB RAM first.
Choose 64 GB+ RAM first.
NVIDIA or AMD · 16–32 GB GPU / 64 GB RAM
Start at 16K. Try 32K only after checking memory.
Start at 16K. Try 32K only after checking memory.
Start at 16K. Try 32K only after checking memory.
Start at 16K. Try 32K only after checking memory.
Start at 16K. Try 32K only after checking memory.
RunPod · NVIDIA 16 GB GPU / 31 GB RAM
Suggest 8K–16K; actual observed context was not recorded.
Choose 48–64 GB RAM first.
Choose 48–64 GB RAM first.
Choose 64 GB RAM first.
Choose 64 GB+ RAM first.
Native model context is 262,144 tokens, not a default that fits every machine. Start Cline at 16,384 context and 4,096 maximum output tokens; leave room for instructions, files, tool schemas, and history.
YOUR MACHINE · YOUR CODING ASSISTANTSOURCE-LINKED SETUP
Start Strata. Connect Cline. Measure your result.
Choose Windows, Linux or RunPod. Your saved machine selects the matching local route when available. Package selection updates every launch command and memory requirement. Coder is a reduced-expert derivative; full packs require more system RAM.
Choose a package Run its commands Check the result
01
BEFORE YOU BEGIN
Choose your package
Select the weights you want. The instructions, downloads and settings change with your choice.
Save your machine in My Hardware to get an automatic starting choice. You can always choose any package yourself.
CURRENT SETUP
Coder · community-modified weights
Start at 16K context with images off. Plan for 32 GB system RAM (32 GB preferred), separate from GPU VRAM, and 120 GB writable SSD. Coder loaded on the reported 31 GB Pod, below the 32 GB recommendation; chat and agent validation remain pending. AMD requires a supported card and backend.
Freshness 82 out of 100. Repository age: 52 days old, Aug 24, 2026. Measures age, not quality.82/ 100Freshness52 days oldRepository age · Aug 24, 2026
Popularity 88 out of 100. 2,290,500 downloads in 30 days; 6,235 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.88/ 100Popularity2.3M downloads / 30d6.2K lifetime likes · Checked Oct 15, 2026
What do these scores mean?+
Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.
Freshness · release age
A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.
Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.
Choose where you want to run it. The instructions below update to match your selection.
CHOOSE YOUR SETUPWindows
Native Windows PowerShell setup with a supported NVIDIA or AMD GPU. 32 GB system RAM is the documented package recommendation; start at 16K context. Cline connects to localhost. No WSL is required for the Windows launcher.
Other operating systems
Mac: The reviewed Strata installer supports Windows and Linux GPU inference, not native Apple Silicon. A Mac can run VS Code and Cline as a client of the authenticated RunPod or Linux server. Compatibility source
01
Check GPU, memory, driver, and free disk
Use a supported NVIDIA or AMD GPU with at least 12 GB VRAM, 32 GB system RAM (32 GB preferred), and 120 GB free SSD space. These are capacity recommendations, not measured speed. For NVIDIA, run nvidia-smi and check driver 580+ for the default engine. For AMD, check your exact Radeon card and the installer’s supported HIP backend in the linked instructions; do not run NVIDIA commands. Use Task Manager → Performance for RAM/VRAM and File Explorer → This PC for free disk. Close GPU-heavy apps before starting.
Install Git for Windows if needed, open a fresh PowerShell window, and run the following in a folder on your SSD. The native launcher installs its supported runtime; you do not need to create a WSL environment.
git clone https://github.com/Niko1221/Strata.git
cd Strata
git checkout fb58e0dbc8399662c0e47c76578c6e878b14f6cf
03
Start Coder at 16K context
Run this in the same PowerShell window. It selects family coder, pack IQ1_M, disables images, and lets the installer choose supported RAM/offload settings. Expect 58.4 GB + ~6 GB draft before caches or optimized files. Keep the server window open. This binds only to your own computer. If memory is tight, stop with Ctrl+C and rerun with --context 8192 and --low-ram on.
.\START-HERE.bat --setup --family coder --model IQ1_M --context 16384 --vision no --low-ram auto --host 127.0.0.1 --port 8080
04
Open chat and confirm the loaded model
Visit http://127.0.0.1:8080 in your browser and send a short chat message. Open a second PowerShell window for these checks. /health must say loaded: true. Copy the exact ID from /v1/models. Monitor and /metrics should show the active runtime, not a failed or still-loading server.
In that second PowerShell window, send a short request using the actual served ID. Check the generated function and tests for correctness. A successful HTTP response verifies inference; the supervised Cline task below verifies file and terminal tool behavior.
PowerShell
$strataModelId = (Invoke-RestMethod http://127.0.0.1:8080/v1/models).data[0].id
$strataBody = @{ model=$strataModelId; messages=@(@{ role='user'; content='Write a short Python palindrome function and two tests.' }); max_tokens=512; stream=$false } | ConvertTo-Json -Depth 6
Invoke-RestMethod -Uri http://127.0.0.1:8080/v1/chat/completions -Method Post -ContentType 'application/json' -Body $strataBody | ConvertTo-Json -Depth 8
06
Connect VS Code and Cline
On your own Windows, Mac, or Linux computer, install VS Code and the official Cline extension. Open Cline Settings and enter the values below. The Base URL ends in /v1; do not append /chat/completions. Confirm Cline is using your Strata provider, not a different remote model. Keep approvals manual. For an existing installation, read its active context setting instead of assuming it used this guide’s suggested 16K.
Setting
Value
Provider
OpenAI Compatible
Base URL
http://127.0.0.1:8080/v1 — same computer
API key
Configured key, or a non-secret placeholder if required for this unkeyed localhost route
Model ID
Exact ID returned by /v1/models
Context window
Match the active server. 16,384 is a starting suggestion; use 8,192 if configured there.
Max output tokens
Start at 4,096 only if it fits inside the context budget; reduce for a short context.
Open an empty, disposable Git repository in VS Code and give Cline the prompt below. Inspect and approve its file edits. Open counter.html in a browser: it should show 0; two clicks should show 2. Confirm Cline invoked tools and saved the file, rather than merely suggesting code in chat. Then ask it to add a Python CSV parser with three unit tests and run those tests after your approval. Review the Git diff, test results, retries and total elapsed time for Coder.
Plain text
In this folder create counter.html, a self-contained web page displaying 0 and a button that increments the count. Ask before edits or shell commands. After saving, explain how to test it.
08
Inspect changes and stop the server
Review the generated files and test results, then use the shared benchmark checklist below to record your hardware and timing. In VS Code Source Control, confirm only the intended files changed. When finished, press Ctrl+C in the Strata server window. Downloaded model files stay on your disk for next time.
03
MORE WAYS TO USE THIS SETUP
Check the API from the computer running Cline
Use the matching shell below. Port 18080 is your open SSH tunnel; use 8080 for a same-machine server, or the exposed HTTPS address for a new keyed Pod. The Jupyter browser URL is not the Cline API.
01
Windows · PowerShell
Keep the SSH tunnel open in a separate window. This checks health, reads the actual model ID and asks for a short response. Enter your Strata key privately, or press Enter only for an unkeyed private connection. Expect JSON and an answer. If authentication fails, resolve the key or tunnel before opening Cline.
PowerShell
$strataBase = 'http://127.0.0.1:18080'
$strataSecret = Read-Host 'Strata API key (Enter only if none configured)' -AsSecureString
$strataHeaders = @{}
try {
if ($strataSecret.Length -gt 0) { $strataHeaders.Authorization = 'Bearer ' + [Net.NetworkCredential]::new('', $strataSecret).Password }
Invoke-RestMethod "$strataBase/health"
$strataModels = Invoke-RestMethod "$strataBase/v1/models" -Headers $strataHeaders
$strataModelId = $strataModels.data[0].id
if (-not $strataModelId) { throw 'No model ID returned; check loading and authentication.' }
$strataModelId
$strataBody = @{model=$strataModelId;messages=@(@{role='user';content='Reply with one short sentence.'});max_tokens=128;stream=$false} | ConvertTo-Json -Depth 6
Invoke-RestMethod "$strataBase/v1/chat/completions" -Method Post -Headers $strataHeaders -ContentType 'application/json' -Body $strataBody | ConvertTo-Json -Depth 8
} finally {
$strataHeaders.Clear()
$strataSecret.Dispose()
}
02
Mac / Linux · Bash terminal
On macOS, type bash first if your Terminal uses zsh. Keep the SSH tunnel open. Enter the configured Strata key at the hidden prompt. This reads the served model ID before sending a request. A successful response is a connection check, not proof that Cline tools work.
STRATA_BASE='http://127.0.0.1:18080'
read -rsp 'Strata API key (Enter only if none configured): ' STRATA_API_KEY
printf '\n'
STRATA_AUTH=()
if [ -n "$STRATA_API_KEY" ]; then STRATA_AUTH=(-H "Authorization: Bearer $STRATA_API_KEY"); fi
curl -fsS "$STRATA_BASE/health"
MODEL_ID=$(curl -fsS "${STRATA_AUTH[@]}" "$STRATA_BASE/v1/models" | python3 -c 'import json,sys; print(json.load(sys.stdin)["data"][0]["id"])')
export MODEL_ID
if [ -n "$MODEL_ID" ]; then
printf '%s\n' "$MODEL_ID"
python3 -c 'import json,os; print(json.dumps({"model":os.environ["MODEL_ID"],"messages":[{"role":"user","content":"Reply with one short sentence."}],"max_tokens":128,"stream":False}))' | curl -fsS "${STRATA_AUTH[@]}" "$STRATA_BASE/v1/chat/completions" -H 'Content-Type: application/json' --data-binary @-
fi
unset STRATA_API_KEY STRATA_AUTH
04
MORE WAYS TO USE THIS SETUP
Understand the settings before increasing them
The easiest reliable route is a short, working coding task first. Large context, more reasoning, and larger model packs are separate experiments.
01
Match the context budget
Start the server and Cline at 16,384 tokens. If memory is tight, use 8,192 on both and reduce maximum output tokens. A 4,096 output allowance is part of the budget, not extra space. Try 32K only with spare RAM/VRAM. New context settings require restarting/reconfiguring the server.
02
Test tool calls and streaming, not just chat
The Cline task must edit files through its tools, run tests after your approval, and finish with a result. If it only prints tool syntax, or stops before applying changes, inspect the server log and Cline provider configuration. Reduce task scope and verify the exact model ID before retrying.
Keep images disabled for these memory estimates. If your runtime/client exposes reasoning controls, start low or off to check connectivity, then compare quality with a higher setting. The reviewed runtime serves one active generation request by default, so several simultaneous coding agents can queue rather than run faster.
05
MORE WAYS TO USE THIS SETUP
Record a benchmark you can reproduce
The publisher’s RTX 5070 12 GB / Ryzen 5 7600 / 64 GB RAM results are not measurements of the RTX 2000 Ada / 31 GB Pod. Show your own Monitor results, including slow or failed runs.
01
Measure cold and warm requests separately
Record the first run after startup, then repeat the identical prompt three times with the model warmed. Log time to first token, prompt-processing (prefill) tokens/sec, output (decode) tokens/sec, prompt/output token counts, total request time, peak VRAM/RAM, and errors. Record context, reasoning, family, pack, and source revision.
02
Capture the machine and runtime
For NVIDIA machines, read GPU usage with nvidia-smi. Windows users can capture RAM in Task Manager; Linux/RunPod users can use free -h and df -h. Open Strata Monitor and /metrics for runtime measurements. Record the Pod allocation and actual CPU/SSD details too; host RAM reported inside a container may not be your allocation.
Fill this out for the actual selected setup. Include waits for tools and manual approvals in agent end-to-end time. Never substitute a chat-only token rate for coding-task completion.
Strata reports loaded: true, browser chat produces a coherent answer, /v1/models and /metrics respond, and the API request succeeds. Cline saves counter.html and two clicks show 2. Its Python CSV follow-up passes three tests and leaves an inspectable Git diff. You have recorded cold/warm speed and actual memory use. For RunPod, stop compute when finished and check remaining storage charges.
Source reviewed October 10, 2026 at the linked revision. The October 9 Pod observations were supplied by the user, not independently reproduced by YouRunAI. Loading, local health and dashboard access were reported; browser chat after the origin fix, external Cline tool use and sustained serving speed remain unconfirmed. Cloud rental sends prompts and code to that server. Local Windows/Linux serving stays on your computer when using the localhost bind. RunPod links are affiliate links.
Setup refuses the GPU or reports a CUDA/driver error.
For NVIDIA, check nvidia-smi and the documented 580+ driver requirement. Template CUDA and host drivers differ. On RunPod choose a compatible host; a container normally cannot replace its host driver. For AMD check the exact card and HIP backend against the pinned install documentation.
The 31 GB RAM Pod runs out of memory.
Confirm the launch command selects --family coder --model IQ1_M, not a full pack. Keep --vision no and --low-ram on. Reduce context to 8192 and match Cline; check the cgroup memory cap, actual free RAM, and SSD space. If it still fails, rent more RAM rather than treating 16 GB VRAM as sufficient.
Generation pauses or output tokens/sec is unexpectedly low.
Check Monitor, RAM pressure, SSD expert reads, CPU contention, and free storage. Compare three warmed requests with the cold run. More RAM or a faster local SSD may matter more than GPU VRAM; the 5070/64 GB benchmark does not predict a 31 GB Pod.
Local /health works but the external 8080 link fails.
Check Connect → HTTP services. The reported Pod exposed only Jupyter 8888; local health does not expose a public 8080 service. Use the documented Jupyter browser proxy and a separate SSH tunnel for Cline. Do not redeploy without backing up /Strata.
The API or browser returns 401 or an origin error.
Use the same API key configured at launch; enter it under Strata About → Settings and in Cline. Keep authentication enabled for any public bind. If an origin restriction rejects the proxy, configure its exact origin using the pinned docs, or use the SSH tunnel instead.
Cline cannot find the model, truncates output, or fails on tools.
Read /v1/models and use its exact ID. Base URL ends in /v1. Match server/client context and leave output headroom. Confirm a short Chat Completions request works, inspect tool/streaming errors, and retry the small CSV task before increasing complexity.
The first start takes a long time or downloads were interrupted.
The installer downloads tens of gigabytes and prepares model files. Reattach to tmux and inspect logs before restarting. If preparation completed, use the saved launcher; if downloads were interrupted, the installer supports resuming them. Check free space in the actual installation and data directories.
Browser terminal says Connection Closed.
Reconnect and run tmux ls. Check setup/calibration and the model server separately. If only calibration stopped, serve with the saved run script in tmux; do not repeat downloads or calibration unnecessarily.
No setup.py or calibrate.py appears in the process check.
Those processes are absent; the serving process may still be running. Check /health, tmux and the port-8080 listener before launching anything else. In the reported interruption, completion or saving of the tune was not confirmed.
Several run-*.sh files appear, or port 8080 is already in use.
Ignore scripts under bench/results and find the model’s top-level generated launcher. The observed Coder one was /Strata/run-coder-iq1_m.sh. Reattach to the existing session; do not start duplicate Strata servers on port 8080.
The Jupyter /proxy/8080/ address returns 404.
Install jupyter-server-proxy in Jupyter’s own Python environment, verify enabled and OK, then restart only Jupyter so it loads the extension. Keep the URL’s trailing slash. Restarting the entire Pod is unnecessary and can lose ephemeral files.
Jupyter asks for a token, or its token changed after restart.
Run jupyter server list in a private off-camera terminal; the output can contain secrets. Use the current token in the login screen. Preserve the original startup arguments and environment on restart. Do not disable authentication or publish the token.
Dashboard loads, but chat returns the web-pages / host / origin 403.
Back up the actual model config and add the exact trusted_origins origin, with no path or wildcard; restart Strata with its saved launcher. Enter any configured API key in About → Settings. If still blocked, check the actual Host and Origin in logs. The reported post-fix chat result remains unconfirmed.
Cline gets an HTML Jupyter login page instead of JSON.
The browser proxy uses Jupyter cookies/tokens; it is not an ordinary OpenAI-compatible API endpoint. Use full SSH forwarding to http://127.0.0.1:18080/v1, or a newly provisioned, authenticated 8080 HTTP service. Ignore the About tab’s incorrect API URL when browsing through Jupyter.
The RunPod HTTP proxy times out with 524.
Long prefill can exceed the proxy’s 100-second timeout. Use a full SSH tunnel and the local 18080 Base URL. Check a small prompt first, then reduce context or inspect CPU/RAM/SSD bottlenecks.
Calibration has already taken more than 30 minutes.
The 15–30 minute estimate covers calibration alone, after download and preparation. It runs multiple rounds and can take longer with six vCPUs. Keep it in tmux, or skip optional tuning and use the saved launcher. Candidate token rates are not serving benchmarks.
groups: cannot find name for group ID 109.
This was a benign container group-name lookup warning in the supplied field report, unrelated to Strata inference. Diagnose actual model errors separately rather than reinstalling solely for this message.
REFERENCE LIBRARY
Sources and files
Original instructions, model files, and compatibility notes behind this setup.