❌Won't fitfp16 · 4K ctx · batch 1
Does RTX 3090 run Mixtral-8x7B-v0.1? No at fp16.
At fp16 precision, Mixtral-8x7B-v0.1 (47 B MoE parameters) needs roughly 111.2 GB of VRAM. RTX 3090 only has 24 GB, short by 87.2 GB. INT4 quantization brings it down to 28.9 GB. For fp16 accuracy, move to H200.
463%
VRAM
Won't fit
Estimated VRAM (fp16)
111.2 GB
of 24 GB on RTX 3090· 87.2 GB short
Tok/sec
39
TFTT
3146ms
Power
198W
Rent on GPUniq marketplace
RTX 3090
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Shared Weights
14.00 GBExpert Weights
93.40 GBKV Cache
0.50 GBActivations
2.00 GBFramework Overhead
1.27 GBQuantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on RTX 3090? |
|---|---|---|---|
| FP16 (full precision) | 111.2 GB | 463% | ❌Won't fit |
| INT8 (8-bit) | 56.3 GB | 235% | ❌Won't fit |
| INT4 / Q4 (4-bit) | 28.9 GB | 120% | ❌Won't fit |
Frequently asked
- Can RTX 3090 run Mixtral-8x7B-v0.1?
- Not at fp16 — Mixtral-8x7B-v0.1 needs about 111.2 GB while RTX 3090 has 24 GB. It fits at INT4 quantization (28.9 GB) with some accuracy trade-off. For full precision, use H200 instead.
- How much VRAM does Mixtral-8x7B-v0.1 use?
- About 111.2 GB at fp16, 28.9 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
- What's the fastest way to run Mixtral-8x7B-v0.1 on RTX 3090?
- Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For RTX 3090, enable Flash Attention 2.
- Where can I rent a RTX 3090?
- GPUniq aggregates live RTX 3090 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for Mixtral-8x7B-v0.1
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None of the common quantization levels fit this model on RTX 3090. Consider multi-GPU deployment or a larger card.