NVIDIA24 GB VRAM1,008 GB/s$0.4/hr

Qwen2 7B on RTX 4090 24GB: Does It Fit, How Fast, What It Costs

Qwen2 7B at FP16 needs 15.2 GB for its weights and 19.0 GB in total at 8K context with 8 concurrent users. That means it fits on a single RTX 4090 24GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
19.0 GB needed / 24 GB available
Decode speed
42.99
tok/s per stream
Aggregate
515.9
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2154
1× RTX 4090 24GB
What changes as you add RTX 4090 24GBs
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× RTX24 GB19.0 GB42.99515.93.08 s$0.2154✓
2× RTX48 GB19.0 GB73.0917541.56 s$0.1267✓
4× RTX96 GB19.0 GB146.27016792.6 ms$0.0633✓
8× RTX192 GB19.0 GB292.328065411.3 ms$0.0317✓
Concurrency vs context length
On 1× RTX 4090 24GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K37240.5 MB✓ fits
8K18481.0 MB✓ fits
33K41.88 GB✓ fits
66K23.76 GB✓ fits
131K17.52 GB✓ fits
Other GPUs that run Qwen2 7B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×479.1$0.1450
RTX 5090 32GB1×917.2$0.1514
AMD MI300X 192GB1×2713$0.2560
V100 SXM2 32GB1×460.6$0.3015
A100 40GB SXM41×793.3$0.3151
AMD MI325X 288GB1×3071$0.3166
Frequently asked questions

Can Qwen2 7B run on RTX 4090 24GB?

Yes. Qwen2 7B at FP16 needs 19.0 GB including the KV cache, and RTX 4090 24GB has 24 GB — so it fits on a single GPU.

How fast is Qwen2 7B on RTX 4090 24GB?

42.99 tokens/sec per stream and 515.9 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.08 s, and inter-token latency is 23.3 ms.

What does Qwen2 7B on RTX 4090 24GB cost per million tokens?

$0.2154 per 1M output tokens at 1× RTX 4090 24GB ($0.4000/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen2 7B need on RTX 4090 24GB?

15.2 GB for the weights at FP16, plus 3.8 GB of KV cache for 8 concurrent requests at 8K context — 19.0 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX 4090 24GB the right choice for Qwen2 7B?

It works. Compare $0.2154 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0840, and the fastest is 1× Cerebras CS-3 (WSE-3) at 10236 tok/s.

Adjust the workload yourself

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

Open in calculator