Compatibility check
Can you run Mistral 7B Instruct v0.3 on the NVIDIA L4?
Yes. Mistral 7B Instruct v0.3 fits on the NVIDIA L4 (24 GB) in full FP16/BF16 precision, using about 17.2 GB including a 0.5 GB KV cache at 4,096 tokens. You have comfortable headroom for longer prompts and modest batching.
Memory breakdown
Weights plus a 0.5 GB KV cache at 4,096tokens, against the card's 24 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 24 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 16.7 GB | 0.5 GB | 17.2 GB | 72% | Fits |
| INT8 (8-bit)near-full quality | 8.3 GB | 0.5 GB | 8.8 GB | 37% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 4.2 GB | 0.5 GB | 4.7 GB | 20% | Fits |
Planning estimates, not a substitute for profiling. Real usage varies with the inference runtime, batch size, and how much context you actually use — the KV cache grows linearly with prompt length.
GPUs that run Mistral 7B Instruct v0.3
Cards where this model fits (at its best precision):
Models that fit the NVIDIA L4
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the NVIDIA L4 run Mistral 7B Instruct v0.3?
Yes. In FP16 it uses about 17.2 GB, which fits the NVIDIA L4's 24 GB.
How much VRAM does Mistral 7B Instruct v0.3 need?
Approximately 16.7 GB in FP16, 8.3 GB in INT8, and 4.2 GB in 4-bit for the weights, plus a KV cache of about 0.5 GB at 4,096 tokens.
Does quantization let Mistral 7B Instruct v0.3 fit on the NVIDIA L4?
Yes. Dropping to FP16 / BF16 brings total usage to about 17.2 GB, which fits the 24 GB card with headroom for the KV cache.
What happens to memory with longer context?
The KV cache grows linearly with prompt length. At 4,096 tokens it is about 0.5 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.