NVIDIA48 GB VRAM960 GB/s$0.8/hr

Mistral 7B v0.3 on RTX 6000 Ada 48GB: Does It Fit, How Fast, What It Costs

Mistral 7B v0.3 at FP16 needs 14.5 GB for its weights and 23.1 GB in total at 8K context with 8 concurrent users. That means it fits on a single RTX 6000 Ada 48GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
23.1 GB needed / 48 GB available
Decode speed
43.09
tok/s per stream
Aggregate
517.1
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4297
1× RTX 6000 Ada 48GB
What changes as you add RTX 6000 Ada 48GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× RTX48 GB23.1 GB43.09517.12.64 s$0.4297✓
2× RTX96 GB23.1 GB73.2617581.34 s$0.2528✓
4× RTX192 GB23.1 GB146.57033684.3 ms$0.1264✓
8× RTX384 GB23.1 GB293.028132357.2 ms$0.0632✓
Concurrency vs context length
On 1× RTX 6000 Ada 48GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K62549.8 MB✓ fits
8K311.07 GB✓ fits
33K74.29 GB✓ fits
66K38.59 GB✓ fits
131K117.18 GB✓ fits
Other GPUs that run Mistral 7B v0.3
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×504.2$0.1377
RTX 5090 32GB1×965.3$0.1439
RTX 4090 24GB1×543.0$0.2046
AMD MI300X 192GB1×2855$0.2432
V100 SXM2 32GB1×484.8$0.2865
A100 40GB SXM41×834.9$0.2994
Frequently asked questions

Can Mistral 7B v0.3 run on RTX 6000 Ada 48GB?

Yes. Mistral 7B v0.3 at FP16 needs 23.1 GB including the KV cache, and RTX 6000 Ada 48GB has 48 GB — so it fits on a single GPU.

How fast is Mistral 7B v0.3 on RTX 6000 Ada 48GB?

43.09 tokens/sec per stream and 517.1 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.64 s, and inter-token latency is 23.2 ms.

What does Mistral 7B v0.3 on RTX 6000 Ada 48GB cost per million tokens?

$0.4297 per 1M output tokens at 1× RTX 6000 Ada 48GB ($0.8000/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Mistral 7B v0.3 need on RTX 6000 Ada 48GB?

14.5 GB for the weights at FP16, plus 8.6 GB of KV cache for 8 concurrent requests at 8K context — 23.1 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX 6000 Ada 48GB the right choice for Mistral 7B v0.3?

It works. Compare $0.4297 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0399, and the fastest is 1× Cerebras CS-3 (WSE-3) at 10773 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator