NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

BGE-M3 (embed, 1024-dim) on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

BGE-M3 (embed, 1024-dim) at FP16 needs 1.1 GB for its weights and 7.6 GB in total at 8K context with 8 concurrent users. That means it fits on a single A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
7.6 GB needed / 80 GB available
Decode speed
1144
tok/s per stream
Aggregate
13732
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0243
1× A100 80GB SXM4
What changes as you add A100 80GB SXM4s
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× A10080 GB7.6 GB11441373290.5 ms$0.0243✓
2× A100160 GB7.6 GB19454669060.3 ms$0.0143✓
4× A100320 GB7.6 GB389118676145.2 ms$0.007139✓
8× A100640 GB7.6 GB778274704237.6 ms$0.003570✓
Concurrency vs context length
On 1× A100 80GB SXM4 at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K195412.3 MB✓ fits
8K97824.6 MB✓ fits
33K243.22 GB✓ fits
66K126.44 GB✓ fits
131K612.88 GB✓ fits
Other GPUs that run BGE-M3 (embed, 1024-dim)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3080 10GB1×5218$0.0106
RTX 3090 24GB1×6427$0.0108
RTX 5090 32GB1×12304$0.0113
RTX 4090 24GB1×6921$0.0161
RTX 4080 16GB1×4923$0.0169
AMD MI300X 192GB1×36391$0.0191
Frequently asked questions

Can BGE-M3 (embed, 1024-dim) run on A100 80GB SXM4?

Yes. BGE-M3 (embed, 1024-dim) at FP16 needs 7.6 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is BGE-M3 (embed, 1024-dim) on A100 80GB SXM4?

1144 tokens/sec per stream and 13732 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 90.5 ms, and inter-token latency is 873.8 µs.

What does BGE-M3 (embed, 1024-dim) on A100 80GB SXM4 cost per million tokens?

$0.0243 per 1M output tokens at 1× A100 80GB SXM4 ($1.20/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does BGE-M3 (embed, 1024-dim) need on A100 80GB SXM4?

1.1 GB for the weights at FP16, plus 6.4 GB of KV cache for 8 concurrent requests at 8K context — 7.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for BGE-M3 (embed, 1024-dim)?

It works. Compare $0.0243 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 1× RTX 3080 10GB at $0.0106, and the fastest is 1× Cerebras CS-3 (WSE-3) at 137324 tok/s.

Adjust the workload yourself

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

Open in calculator