Cerebras44 GB VRAM20,000 GB/s

Qwen 2.5-VL 7B (VLM) on Cerebras CS-3 (WSE-3): Does It Fit, How Fast, What It Costs

Qwen 2.5-VL 7B (VLM) at FP16 needs 14.0 GB for its weights and 17.8 GB in total at 8K context with 8 concurrent users. That means it fits on a single Cerebras CS-3 (WSE-3). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
17.8 GB needed / 44 GB available
Decode speed
928.6
tok/s per stream
Aggregate
11143
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Cerebras CS-3 (WSE-3) · quote required
What changes as you add Cerebras CS-3 (WSE-3)s
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× Cerebras44 GB17.8 GB928.611143214.6 ms—✓
2× Cerebras88 GB17.8 GB157937886122.4 ms—✓
4× Cerebras176 GB17.8 GB315715154376.2 ms—✓
8× Cerebras352 GB17.8 GB631460617153.1 ms—✓
Concurrency vs context length
On 1× Cerebras CS-3 (WSE-3) at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K127240.5 MB✓ fits
8K63481.0 MB✓ fits
33K151.88 GB✓ fits
66K73.76 GB✓ fits
131K37.52 GB✓ fits
Other GPUs that run Qwen 2.5-VL 7B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×521.5$0.1332
RTX 5090 32GB1×998.4$0.1391
RTX 4090 24GB1×561.6$0.1978
AMD MI300X 192GB1×2953$0.2352
V100 SXM2 32GB1×501.4$0.2770
A100 40GB SXM41×863.6$0.2895
Frequently asked questions

Can Qwen 2.5-VL 7B (VLM) run on Cerebras CS-3 (WSE-3)?

Yes. Qwen 2.5-VL 7B (VLM) at FP16 needs 17.8 GB including the KV cache, and Cerebras CS-3 (WSE-3) has 44 GB — so it fits on a single GPU.

How fast is Qwen 2.5-VL 7B (VLM) on Cerebras CS-3 (WSE-3)?

928.6 tokens/sec per stream and 11143 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 214.6 ms, and inter-token latency is 1.1 ms.

What does Qwen 2.5-VL 7B (VLM) on Cerebras CS-3 (WSE-3) cost per million tokens?

— per 1M output tokens at 1× Cerebras CS-3 (WSE-3). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen 2.5-VL 7B (VLM) need on Cerebras CS-3 (WSE-3)?

14.0 GB for the weights at FP16, plus 3.8 GB of KV cache for 8 concurrent requests at 8K context — 17.8 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Cerebras CS-3 (WSE-3) the right choice for Qwen 2.5-VL 7B (VLM)?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0772, and the fastest is 1× Cerebras CS-3 (WSE-3) at 11143 tok/s.

Adjust the workload yourself

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

Open in calculator