Cerebras44 GB VRAM20,000 GB/s

GTE Large (1024-dim) on Cerebras CS-3 (WSE-3): Does It Fit, How Fast, What It Costs

GTE Large (1024-dim) at FP16 needs 0.9 GB for its weights and 7.3 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
7.3 GB needed / 44 GB available
Decode speed
14773
tok/s per stream
Aggregate
177273
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 GB7.3 GB1477317727341.6 ms—✓
2× Cerebras88 GB7.3 GB2511460272735.8 ms—✓
4× Cerebras176 GB7.3 GB50227241090932.9 ms—✓
8× Cerebras352 GB7.3 GB100455964363631.5 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?
4K107412.3 MB✓ fits
8K53824.6 MB✓ fits
33K133.22 GB✓ fits
66K66.44 GB✓ fits
131K312.88 GB✓ fits
Other GPUs that run GTE Large (1024-dim)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3080 10GB1×6736$0.008247
RTX 3090 24GB1×8296$0.008370
RTX 5090 32GB1×15884$0.008744
RTX 4090 24GB1×8935$0.0124
RTX 4080 16GB1×6355$0.0131
AMD MI300X 192GB1×46977$0.0148
Frequently asked questions

Can GTE Large (1024-dim) run on Cerebras CS-3 (WSE-3)?

Yes. GTE Large (1024-dim) at FP16 needs 7.3 GB including the KV cache, and Cerebras CS-3 (WSE-3) has 44 GB — so it fits on a single GPU.

How fast is GTE Large (1024-dim) on Cerebras CS-3 (WSE-3)?

14773 tokens/sec per stream and 177273 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 41.6 ms, and inter-token latency is 67.7 µs.

What does GTE Large (1024-dim) 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 GTE Large (1024-dim) need on Cerebras CS-3 (WSE-3)?

0.9 GB for the weights at FP16, plus 6.4 GB of KV cache for 8 concurrent requests at 8K context — 7.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Cerebras CS-3 (WSE-3) the right choice for GTE Large (1024-dim)?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 1× RTX 3080 10GB at $0.008247, and the fastest is 1× Cerebras CS-3 (WSE-3) at 177273 tok/s.

Adjust the workload yourself

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

Open in calculator