DeepSeek Coder V2 236B Q5 VRAM Requirement and Ollama Model Size
DeepSeek Coder V2 236B Q5 needs 140GB minimum VRAM, 150GB optimal VRAM, and 128GB+ system RAM for a safer Ollama run. Popular Ollama model family: DeepSeek Coder V2. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | DeepSeek Coder V2 |
|---|---|
| Scenario | coding |
| License scope | open-source |
| Quantization | Q5 |
| VRAM minimum | 140GB |
| VRAM optimal | 150GB |
| Best local GPU | Cloud-first (no practical single-GPU local) |
| Cloud fallback | H100/H200 class |
| Updated | 2026-02-24 |
| Data status | Estimated baseline (pending measurement) |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | deepseek-coder-v2:236b |
| Category | coding |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 1.7 |
| RTX 4090 24GB | 2.3 |
| A100 80GB | 4.1 |
Quick Answers
- How much VRAM does DeepSeek Coder V2 236B Q5 need?
- DeepSeek Coder V2 236B Q5 needs about 140GB minimum VRAM and 150GB optimal VRAM for a safer local run target.
- Can DeepSeek Coder V2 236B Q5 run on an RTX 3090 24GB?
- Not as a comfortable local target. DeepSeek Coder V2 236B Q5 needs about 140GB minimum and 150GB optimal VRAM, so cloud or larger local GPUs are safer.
- What is the Ollama command for DeepSeek Coder V2 236B Q5?
- Use ollama run deepseek-coder-v2:236b. Check the Ollama tag deepseek-coder-v2:236b before running.
- What is the approximate Ollama model size for DeepSeek Coder V2 236B Q5?
- DeepSeek Coder V2 236B Q5 uses the deepseek-coder-v2:236b Ollama tag. Treat the Q5 236 profile as smaller than the runtime VRAM budget; use 140GB minimum VRAM and 150GB optimal VRAM as the safer sizing numbers.
- How much system RAM does DeepSeek Coder V2 236B Q5 need in Ollama?
- Plan for at least 128GB system RAM alongside 140GB minimum VRAM and 150GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for DeepSeek Coder V2 236B Q5?
- DeepSeek Coder V2 236B Q5 should be sized by GPU VRAM first: 140GB minimum and 150GB optimal. System RAM should be at least 128GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is DeepSeek Coder V2 236B Q5 VRAM usage on an RTX 3090?
- DeepSeek Coder V2 236B Q5 is not recommended for an RTX 3090 24GB target. The page estimates 140GB minimum and 150GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the deepseek-coder-v2:236b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: DeepSeek Coder V2 236B Q5 (Q5, 140GB min/150GB optimal); DeepSeek Coder V2 236B Q4 (Q4, 138GB min/148GB optimal); DeepSeek Coder V2 236B Q8 (Q8, 144GB min/154GB optimal); DeepSeek Coder V2 236B FP16 (FP16, 150GB min/162GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is DeepSeek Coder V2 236B Q5 benchmark data measured or estimated?
- DeepSeek Coder V2 236B Q5 currently uses estimated baseline anchors on this page. Verified benchmark data will replace the estimate after a local hardware run is available.
Ollama Model Size and RAM Notes
| Ollama tag | deepseek-coder-v2:236b |
|---|---|
| Run command | ollama run deepseek-coder-v2:236b |
| Model size guidance | Q5 quantization, 236 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 140GB minimum, 150GB optimal |
| System RAM planning | 128GB or more when running this tag locally with Ollama |
Download size alone is not a safe runtime budget. Runtime memory also includes KV cache, context length, and GPU layer placement.
Ollama RAM vs VRAM Requirements
| GPU VRAM usage estimate | 140GB minimum / 150GB optimal |
|---|---|
| System RAM requirement | 128GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Not recommended |
| CPU spill risk | High without larger GPU or cloud |
| Context warning | Longer context increases KV cache memory, so real VRAM usage can exceed short-prompt estimates. |
For Ollama, VRAM is the first fit check. System RAM matters when layers spill to CPU, when context grows, or when the local app stack runs beside the model.
Available Tag Variants and VRAM Targets
| Profile | Quantization | VRAM target | Ollama tag |
|---|---|---|---|
| DeepSeek Coder V2 236B Q5 | Q5 | 140GB min / 150GB optimal | deepseek-coder-v2:236b |
| DeepSeek Coder V2 236B Q4 | Q4 | 138GB min / 148GB optimal | deepseek-coder-v2:236b |
| DeepSeek Coder V2 236B Q8 | Q8 | 144GB min / 154GB optimal | deepseek-coder-v2:236b |
| DeepSeek Coder V2 236B FP16 | FP16 | 150GB min / 162GB optimal | deepseek-coder-v2:236b |
Ollama library tags often group multiple quantization choices under one model family. LocalVRAM separates them into VRAM profiles so you can pick the right local target.
Real Hardware Benchmark (RTX 3090)
Real benchmark data not available yet for this tag. Estimated anchors are shown above.
Performance Curve
Reference anchors are baseline estimates. Measured RTX 3090 data is overlaid when available.
Best Hardware for DeepSeek Coder V2 236B Q5
- Local run: RTX 3090 (24GB) (Check latest deal) for around 1.7 tok/s on this profile.
- Cloud run: RunPod H100/H200 class , about 2.4x the local 3090 speed anchor.
- Alternative cloud: Vast.ai options for flexible spot pricing.
Local vs Cloud Cost Hint
| Mode | 40h / month | 120h / month |
|---|---|---|
| Local power only (3090 baseline) | $2.24 | $6.72 |
| H100/H200 class | $196 | $588 |
Related Model Profiles
- DeepSeek Coder V2 236B Q4 138GB min, 148GB optimal
- DeepSeek Coder V2 236B Q8 144GB min, 154GB optimal
- DeepSeek Coder V2 236B FP16 150GB min, 162GB optimal
ollama run deepseek-coder-v2:236b More coding models More 100b-250b models Benchmark changelog Submit your test result Run on RunPod Try Vast.ai We may earn a commission if you click links on this page.
This page currently uses estimated benchmark baselines. Measured data will replace it after validation.