NVIDIA16 GB VRAM320 GB/s$0.2/hr

GTE Large (1024-dim) on T4 16GB: 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 T4 16GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
7.3 GB needed / 16 GB available
Decode speed
236.4
tok/s per stream
Aggregate
2836
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0196
1× T4 16GB
What changes as you add T4 16GBs
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× T416 GB7.3 GB236.428361.81 s$0.0196✓
2× T432 GB7.3 GB401.89644922.5 ms$0.0115✓
4× T464 GB7.3 GB803.638575476.2 ms$0.005761✓
8× T4128 GB7.3 GB1607154298253.1 ms$0.002880✓
Concurrency vs context length
On 1× T4 16GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K37412.3 MB✓ fits
8K18824.6 MB✓ fits
33K43.22 GB✓ fits
66K26.44 GB✓ fits
131K112.88 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 T4 16GB?

Yes. GTE Large (1024-dim) at FP16 needs 7.3 GB including the KV cache, and T4 16GB has 16 GB — so it fits on a single GPU.

How fast is GTE Large (1024-dim) on T4 16GB?

236.4 tokens/sec per stream and 2836 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.81 s, and inter-token latency is 4.2 ms.

What does GTE Large (1024-dim) on T4 16GB cost per million tokens?

$0.0196 per 1M output tokens at 1× T4 16GB ($0.2000/hr). 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 T4 16GB?

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 T4 16GB the right choice for GTE Large (1024-dim)?

It works. Compare $0.0196 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