CodeLlama 34B Q4 VRAM Requirement and Ollama Model Size
CodeLlama 34B Q4 needs 18GB minimum VRAM, 28GB optimal VRAM, and 64GB+ system RAM for a safer Ollama run. Popular Ollama model family: CodeLlama. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | CodeLlama |
|---|---|
| Scenario | coding |
| License scope | closed-weight |
| Quantization | Q4 |
| VRAM minimum | 18GB |
| VRAM optimal | 28GB |
| Best local GPU | RTX 6000 Ada 48GB |
| Cloud fallback | A100 80GB |
| Updated | 2026-02-24 |
| Data status | Estimated baseline (pending measurement) |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | codellama:34b |
| Category | coding |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 11 |
| RTX 4090 24GB | 14.9 |
| A100 80GB | 26.4 |
Quick Answers
- How much VRAM does CodeLlama 34B Q4 need?
- CodeLlama 34B Q4 needs about 18GB minimum VRAM and 28GB optimal VRAM for a safer local run target.
- Can CodeLlama 34B Q4 run on an RTX 3090 24GB?
- It may load on an RTX 3090 24GB because the minimum estimate is 18GB, but the safer target is 28GB. Expect tighter context or lower throughput.
- What is the Ollama command for CodeLlama 34B Q4?
- Use ollama run codellama:34b. Check the Ollama tag codellama:34b before running.
- What is the approximate Ollama model size for CodeLlama 34B Q4?
- CodeLlama 34B Q4 uses the codellama:34b Ollama tag. Treat the Q4 34 profile as smaller than the runtime VRAM budget; use 18GB minimum VRAM and 28GB optimal VRAM as the safer sizing numbers.
- How much system RAM does CodeLlama 34B Q4 need in Ollama?
- Plan for at least 64GB system RAM alongside 18GB minimum VRAM and 28GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for CodeLlama 34B Q4?
- CodeLlama 34B Q4 should be sized by GPU VRAM first: 18GB minimum and 28GB optimal. System RAM should be at least 64GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is CodeLlama 34B Q4 VRAM usage on an RTX 3090?
- CodeLlama 34B Q4 is borderline for an RTX 3090 24GB target. The page estimates 18GB minimum and 28GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the codellama:34b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: CodeLlama 34B Q4 (Q4, 18GB min/28GB optimal); CodeLlama 34B Q5 (Q5, 20GB min/30GB optimal); CodeLlama 34B Q8 (Q8, 24GB min/34GB optimal); CodeLlama 34B FP16 (FP16, 30GB min/42GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is CodeLlama 34B Q4 benchmark data measured or estimated?
- CodeLlama 34B 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 | codellama:34b |
|---|---|
| Run command | ollama run codellama:34b |
| Model size guidance | Q4 quantization, 34 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 18GB minimum, 28GB optimal |
| System RAM planning | 64GB 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 | 18GB minimum / 28GB optimal |
|---|---|
| System RAM requirement | 64GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Borderline |
| 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 |
|---|---|---|---|
| CodeLlama 34B Q4 | Q4 | 18GB min / 28GB optimal | codellama:34b |
| CodeLlama 34B Q5 | Q5 | 20GB min / 30GB optimal | codellama:34b |
| CodeLlama 34B Q8 | Q8 | 24GB min / 34GB optimal | codellama:34b |
| CodeLlama 34B FP16 | FP16 | 30GB min / 42GB optimal | codellama:34b |
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 CodeLlama 34B Q4
- Local run: RTX 3090 (24GB) (Check latest deal) for around 11 tok/s on this profile.
- Cloud run: RunPod A100 80GB , 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 |
| A100 80GB | $78 | $234 |
Related Model Profiles
- CodeLlama 34B Q5 20GB min, 30GB optimal
- CodeLlama 34B Q8 24GB min, 34GB optimal
- CodeLlama 34B FP16 30GB min, 42GB optimal
ollama run codellama:34b More coding models More 30b-34b 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.