Google32 GB VRAM819 GB/s$0.8/hr

GTE Large (1024-dim) on TPU v6e Trillium (32GB): 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 TPU v6e Trillium (32GB). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
7.3 GB needed / 32 GB available
Decode speed
604.9
tok/s per stream
Aggregate
7259
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0306
1× TPU v6e Trillium (32GB)
What changes as you add TPU v6e Trillium (32GB)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× TPU32 GB7.3 GB604.9725947.4 ms$0.0306✓
2× TPU64 GB7.3 GB10282468238.8 ms$0.0180✓
4× TPU128 GB7.3 GB20579872734.4 ms$0.009004✓
8× TPU256 GB7.3 GB411439490732.2 ms$0.004502✓
Concurrency vs context length
On 1× TPU v6e Trillium (32GB) at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K77412.3 MB✓ fits
8K38824.6 MB✓ fits
33K93.22 GB✓ fits
66K46.44 GB✓ fits
131K212.88 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 TPU v6e Trillium (32GB)?

Yes. GTE Large (1024-dim) at FP16 needs 7.3 GB including the KV cache, and TPU v6e Trillium (32GB) has 32 GB — so it fits on a single GPU.

How fast is GTE Large (1024-dim) on TPU v6e Trillium (32GB)?

604.9 tokens/sec per stream and 7259 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 47.4 ms, and inter-token latency is 1.7 ms.

What does GTE Large (1024-dim) on TPU v6e Trillium (32GB) cost per million tokens?

$0.0306 per 1M output tokens at 1× TPU v6e Trillium (32GB) ($0.8000/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 TPU v6e Trillium (32GB)?

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 TPU v6e Trillium (32GB) the right choice for GTE Large (1024-dim)?

It works. Compare $0.0306 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