Skip to content
Run LLM Locally

Mistral 7B Instruct v0.3 on the RTX 4090

With 24 GB of VRAM, the RTX 4090 runs Mistral 7B Instruct v0.3 at Q4_K_M entirely on the GPU with an 8K context (about 5.9 GB needed). At Q8_0 it fits entirely.

ShareFacebookXWhatsAppLinkedInEmail

Mistral 7B Instruct v0.3

Q4_K_M · 8K context

Excellent

The model needs about 5.9 GB and fits in the 23.3 GB available, with 17.3 GB to spare.

Your hardware
RTX 4090 24 GB · 64 GB RAM
Estimated memory
~5.9 GB estimate
VRAM (usable memory)
23.3 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
122–180 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (1,008 GB/s) and model size

Memory breakdown

~5.9 GB (5.6 GB–6.3 GB) needed · estimate

Fits entirely in VRAM · 17.3 GB left

  • Model weights4.1 GB
  • KV cache1 GB
  • Runtime overhead0.6 GB
  • Safety margin0.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the RTX 4090

Mistral 7B Instruct v0.3 on RTX 4090 + 64 GB RAM · 8K context
Mistral 7B Instruct v0.3 Q8_08K~9.2 GB · 80–112 tok/s estimateExcellent
Mistral 7B Instruct v0.3 Q6_K8K~7.5 GB · 98–140 tok/s estimateExcellent
Mistral 7B Instruct v0.3 Q5_K_M8K~6.7 GB · 109–158 tok/s estimateExcellent
Mistral 7B Instruct v0.3 Q4_K_M8K~5.9 GB · 122–180 tok/s estimateExcellent
Mistral 7B Instruct v0.3 IQ4_XS8K~5.5 GB · 131–196 tok/s estimateExcellent
Mistral 7B Instruct v0.3 Q3_K_M8K~5.2 GB · 140–212 tok/s estimateExcellent

Alternatives

GPUs that run Mistral 7B Instruct v0.3 Q4_K_M entirely in VRAM

Other models that fit the RTX 4090

Where to buy a RTX 4090

Prices and links follow the market you choose.

GeForce RTX 4090

24 GB GDDR6X · 1,008 GB/s · 450 W

ExcellentRuns Mistral 7B Instruct v0.3 Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RTX 4090 run Mistral 7B Instruct v0.3?
The model needs about 5.9 GB and fits in the 23.3 GB available, with 17.3 GB to spare.
Which quantization should I use on the RTX 4090?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~9.2 GB / 23.3 GB.
What is the largest context on the RTX 4090?
At Q4_K_M, about 32K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Mistral 7B Instruct v0.3 (RTX 4090)?
We estimate 122–180 tok/s (estimate, confidence low). Based on: memory bandwidth (1,008 GB/s) and model size.