Home / Models / Code Llama 7B Instruct / NVIDIA RTX A4000 16 GB
Can Code Llama 7B Instruct run on NVIDIA RTX A4000 16 GB?
Yes. Calculated memory fit: Code Llama 7B Instruct can run entirely in this GPU’s usable VRAM at one or more supported quantizations.
Can it run?
- Can it run?
- Yes, full GPU fit (calculated estimate).
- Full GPU fit?
- Yes
- Model
- Code Llama 7B Instruct · 6.7B
- GPU
- NVIDIA RTX A4000 16 GB · 16 GB advertised
Memory calculation
Breakdown for Q4 at 8K context, batch size 1. Calculator v1.0.0.
| Model weights | 4.0 GB |
|---|---|
| KV cache | 4.0 GB |
| Runtime reserve | 0.7 GB |
| Safety margin | 0.4 GB |
| Required (estimated) | 9.0 GB |
| GPU usable VRAM | 14.4 GB |
Result: FITS IN VRAM
Quantization table
| Quantization | 2K | 4K | 8K | 16K |
|---|---|---|---|---|
| BF16 | Offload | Offload | Offload | Offload |
| FP16 | Offload | Offload | Offload | Offload |
| Q3 | Fits | Fits | Fits | Fits |
| Q4 | Fits | Fits | Fits | Fits |
| Q5 | Fits | Fits | Fits | Fits |
| Q6 | Fits | Fits | Fits | Limited context |
| Q8 | Fits | Fits | Fits | Limited context |
What fits entirely in VRAM?
- Q3 at 16K context
- Q3 at 2K context
- Q3 at 4K context
- Q3 at 8K context
- Q4 at 16K context
- Q4 at 2K context
- Q4 at 4K context
- Q4 at 8K context
- Q5 at 16K context
- Q5 at 2K context
- Q5 at 4K context
- Q5 at 8K context
What requires RAM offload?
Offload is shown only when the model does not fully fit in usable VRAM but stays within the modeled offload allowance.
Available graphics cards
These SKUs use this GPU chip. Amazon CTAs appear only for EXACT/HIGH matches. Absence of a price does not change the compatibility result above.
Sources
Model: official config codellama/CodeLlama-7b-Instruct-hf (main). GPU/product specs: Icecat. Compatibility: RigForAI calculator v1.0.0 estimated VRAM · Q8 at 8K: FITS_IN_VRAM. Amazon: affiliate commerce match, not a spec source.