DeepSeek Coder 1.3B Q4 VRAM Requirement and Ollama Model Size
DeepSeek Coder 1.3B Q4 needs 2GB minimum VRAM, 12GB optimal VRAM, and 16GB+ system RAM for a safer Ollama run. Popular Ollama model family: DeepSeek Coder. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | DeepSeek Coder |
|---|---|
| Scenario | coding |
| License scope | open-source |
| Quantization | Q4 |
| VRAM minimum | 2GB |
| VRAM optimal | 12GB |
| Best local GPU | RTX 3090 24GB |
| Cloud fallback | A6000 48GB |
| Updated | 2026-02-24 |
| Data status | Estimated baseline (pending measurement) |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | deepseek-coder:1.3b |
| Category | coding |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 42 |
| RTX 4090 24GB | 56.7 |
| A100 80GB | 100.8 |
Quick Answers
- How much VRAM does DeepSeek Coder 1.3B Q4 need?
- DeepSeek Coder 1.3B Q4 needs about 2GB minimum VRAM and 12GB optimal VRAM for a safer local run target.
- Can DeepSeek Coder 1.3B Q4 run on an RTX 3090 24GB?
- Yes. DeepSeek Coder 1.3B Q4 fits comfortably on an RTX 3090 24GB target with 12GB optimal VRAM.
- What is the Ollama command for DeepSeek Coder 1.3B Q4?
- Use ollama run deepseek-coder:1.3b. Check the Ollama tag deepseek-coder:1.3b before running.
- What is the approximate Ollama model size for DeepSeek Coder 1.3B Q4?
- DeepSeek Coder 1.3B Q4 uses the deepseek-coder:1.3b Ollama tag. Treat the Q4 1.3 profile as smaller than the runtime VRAM budget; use 2GB minimum VRAM and 12GB optimal VRAM as the safer sizing numbers.
- How much system RAM does DeepSeek Coder 1.3B Q4 need in Ollama?
- Plan for at least 16GB system RAM alongside 2GB minimum VRAM and 12GB 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 1.3B Q4?
- DeepSeek Coder 1.3B Q4 should be sized by GPU VRAM first: 2GB minimum and 12GB optimal. System RAM should be at least 16GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is DeepSeek Coder 1.3B Q4 VRAM usage on an RTX 3090?
- DeepSeek Coder 1.3B Q4 is comfortable for an RTX 3090 24GB target. The page estimates 2GB minimum and 12GB optimal VRAM, with spill risk marked as low.
- Which LocalVRAM profiles share the deepseek-coder:1.3b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: DeepSeek Coder 1.3B Q4 (Q4, 2GB min/12GB optimal); DeepSeek Coder 1.3B Q5 (Q5, 4GB min/14GB optimal); DeepSeek Coder 1.3B Q8 (Q8, 8GB min/18GB optimal); DeepSeek Coder 1.3B FP16 (FP16, 14GB min/26GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is DeepSeek Coder 1.3B Q4 benchmark data measured or estimated?
- DeepSeek Coder 1.3B Q4 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:1.3b |
|---|---|
| Run command | ollama run deepseek-coder:1.3b |
| Model size guidance | Q4 quantization, 1.3 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 2GB minimum, 12GB optimal |
| System RAM planning | 16GB 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 | 2GB minimum / 12GB optimal |
|---|---|
| System RAM requirement | 16GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Comfortable |
| CPU spill risk | Low |
| 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 1.3B Q4 | Q4 | 2GB min / 12GB optimal | deepseek-coder:1.3b |
| DeepSeek Coder 1.3B Q5 | Q5 | 4GB min / 14GB optimal | deepseek-coder:1.3b |
| DeepSeek Coder 1.3B Q8 | Q8 | 8GB min / 18GB optimal | deepseek-coder:1.3b |
| DeepSeek Coder 1.3B FP16 | FP16 | 14GB min / 26GB optimal | deepseek-coder:1.3b |
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 1.3B Q4
- Local run: RTX 3090 (24GB) (Check latest deal) for around 42 tok/s on this profile.
- Cloud run: RunPod A6000 48GB , 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 |
| A6000 48GB | $30.4 | $91.2 |
Related Model Profiles
- DeepSeek Coder 1.3B Q5 4GB min, 14GB optimal
- DeepSeek Coder 1.3B Q8 8GB min, 18GB optimal
- DeepSeek Coder 1.3B FP16 14GB min, 26GB optimal
ollama run deepseek-coder:1.3b More coding models More tiny-class models Benchmark changelog Submit your test result Check local GPU upgrade 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.