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Code Llama 34B Instruct
34B parameters · native context 16,384 · llama
Estimated VRAM requirement
Weight memory uses parameter count × bits per weight, plus a quantization overhead factor. KV cache uses standard GQA formulas. Values are calculated estimates, not measured allocations.
| Quantization | Estimated weights | 2K total | 4K total | 8K total | 16K total |
|---|---|---|---|---|---|
| FP16 | 64.0 GB | 70.3 GB | 70.6 GB | 71.4 GB | 72.9 GB |
| Q8 | 36.0 GB | 40.0 GB | 40.4 GB | 41.1 GB | 42.6 GB |
| Q6 | 28.1 GB | 31.4 GB | 31.8 GB | 32.5 GB | 34.0 GB |
| Q5 | 23.7 GB | 26.8 GB | 27.1 GB | 27.9 GB | 29.4 GB |
| Q4 | 19.8 GB | 22.5 GB | 22.9 GB | 23.6 GB | 25.1 GB |
| Q3 | 15.8 GB | 18.2 GB | 18.6 GB | 19.3 GB | 20.8 GB |
Find a GPU for this model
Run at the quantization and context selected below. Defaults are Q4 and 8K when you do not change the controls. Results are full-VRAM fits only — not a speed ranking.
- Run at
- Q4 · 8K context
- Required VRAM
- 23.6 GB
No fresh matched Amazon price is currently available for a full-VRAM fit. See compatible GPUs · Workstations · Rent a server · Buy vs rent.
Lowest-cost full-VRAM option
Sorted by lowest current Amazon price among GPUs that fit entirely in VRAM. This is not a performance ranking.
No compatible GPU currently has a fresh Amazon price.
More VRAM headroom
Sorted by leftover usable VRAM after the calculated requirement. Headroom is not tokens/sec.
NVIDIA B200 192 GB
- GPU VRAM
- 192 GB advertised
- Required VRAM
- 23.6 GB
- Usable GPU VRAM
- 172.8 GB
- VRAM headroom
- 149.2 GB — leftover usable memory after the calculated requirement, not a speed ranking
- Compatible
- Yes — fits entirely in VRAM
- Product SKUs
- 0 mapped · 0 Amazon-matched
- Amazon
- Check availability
NVIDIA H200 141 GB
- GPU VRAM
- 141 GB advertised
- Required VRAM
- 23.6 GB
- Usable GPU VRAM
- 126.9 GB
- VRAM headroom
- 103.3 GB — leftover usable memory after the calculated requirement, not a speed ranking
- Compatible
- Yes — fits entirely in VRAM
- Product SKUs
- 0 mapped · 0 Amazon-matched
- Amazon
- Check availability
NVIDIA GH200 96 GB
- GPU VRAM
- 96 GB advertised
- Required VRAM
- 23.6 GB
- Usable GPU VRAM
- 86.4 GB
- VRAM headroom
- 62.8 GB — leftover usable memory after the calculated requirement, not a speed ranking
- Compatible
- Yes — fits entirely in VRAM
- Product SKUs
- 0 mapped · 0 Amazon-matched
- Amazon
- Check availability
Other compatible GPUs
NVIDIA RTX 5000 32 GB
- GPU VRAM
- 32 GB advertised
- Required VRAM
- 23.6 GB
- Usable GPU VRAM
- 28.8 GB
- VRAM headroom
- 5.2 GB — leftover usable memory after the calculated requirement, not a speed ranking
- Compatible
- Yes — fits entirely in VRAM
- Product SKUs
- 2 mapped · 1 Amazon-matched
- Amazon
- Check price on Amazon
NVIDIA Tesla M10 32 GB
- GPU VRAM
- 32 GB advertised
- Required VRAM
- 23.6 GB
- Usable GPU VRAM
- 28.8 GB
- VRAM headroom
- 5.2 GB — leftover usable memory after the calculated requirement, not a speed ranking
- Compatible
- Yes — fits entirely in VRAM
- Product SKUs
- 1 mapped · 0 Amazon-matched
- Amazon
- Check availability
NVIDIA Quadro GV100 32 GB
- GPU VRAM
- 32 GB advertised
- Required VRAM
- 23.6 GB
- Usable GPU VRAM
- 28.8 GB
- VRAM headroom
- 5.2 GB — leftover usable memory after the calculated requirement, not a speed ranking
- Compatible
- Yes — fits entirely in VRAM
- Product SKUs
- 1 mapped · 0 Amazon-matched
- Amazon
- Check availability
Compatible GPUs
Status is a calculated memory fit against usable VRAM (advertised × 0.9). Open a GPU to see product SKUs and Amazon listings.
Compare GPUs for this model
Published chip comparisons where both GPUs fully fit this model at Q4 / 8K. Opens with this model preselected; that query is not indexed.
How fast can this model run?
llama.cpp CUDA decode at Q4 under llama-bench tg128 conditions. Not a ranking. Open a pair for prefill and methodology.
| GPU | Decode | State | Confidence |
|---|---|---|---|
| NVIDIA RTX 5090 32 GB | ~54 tok/s | Estimated | LOW |
| NVIDIA RTX A6000 48 GB | ~26 tok/s | Estimated | LOW |
Sources
Model specifications: official repository codellama/CodeLlama-34b-Instruct-hf · revision main. Compatibility: RigForAI calculator v1.0.0 (estimated VRAM). GPU products: Icecat. Amazon is a separate affiliate match and is not used as a spec source.