Google95 GB VRAM819 GB/s$4.2/hr

Qwen3.8 27B (VLM) on TPU v5p (per-chip, 95GB): Does It Fit, How Fast, What It Costs

Qwen3.8 27B (VLM) at FP8 (E4M3) needs 27.8 GB for its weights and 33.3 GB in total at 8K context with 8 concurrent users. That means it fits on a single TPU v5p (per-chip, 95GB). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
33.3 GB needed / 95 GB available
Decode speed
28.74
tok/s per stream
Aggregate
344.9
tok/s @ 8 concurrent
Cost / 1M tokens
$3.38
1× TPU v5p (per-chip, 95GB)
What changes as you add TPU v5p (per-chip, 95GB)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× TPU95 GB33.3 GB28.74344.92.05 s$3.38✓
2× TPU190 GB33.3 GB48.8611731.04 s$1.99✓
4× TPU380 GB33.3 GB97.734691536.1 ms$0.9948✓
8× TPU760 GB33.3 GB195.518764283.0 ms$0.4974✓
Concurrency vs context length
On 1× TPU v5p (per-chip, 95GB) at FP8 (E4M3). KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K1601.07 GB✓ fits
8K972.15 GB✓ fits
33K298.59 GB✓ fits
66K1517.18 GB✓ fits
131K734.36 GB✓ fits
Other GPUs that run Qwen3.8 27B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2566$0.1083
AMD MI300X 192GB1×2232$0.3111
A100 40GB SXM41×652.8$0.3830
AMD MI325X 288GB1×2527$0.3847
A100 80GB SXM41×842.3$0.3957
Intel Gaudi 3 128GB1×1390$0.3997
Frequently asked questions

Can Qwen3.8 27B (VLM) run on TPU v5p (per-chip, 95GB)?

Yes. Qwen3.8 27B (VLM) at FP8 (E4M3) needs 33.3 GB including the KV cache, and TPU v5p (per-chip, 95GB) has 95 GB — so it fits on a single GPU.

How fast is Qwen3.8 27B (VLM) on TPU v5p (per-chip, 95GB)?

28.74 tokens/sec per stream and 344.9 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.05 s, and inter-token latency is 34.8 ms.

What does Qwen3.8 27B (VLM) on TPU v5p (per-chip, 95GB) cost per million tokens?

$3.38 per 1M output tokens at 1× TPU v5p (per-chip, 95GB) ($4.20/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen3.8 27B (VLM) need on TPU v5p (per-chip, 95GB)?

27.8 GB for the weights at FP8 (E4M3), plus 5.5 GB of KV cache for 8 concurrent requests at 8K context — 33.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is TPU v5p (per-chip, 95GB) the right choice for Qwen3.8 27B (VLM)?

It works. Compare $3.38 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0511, and the fastest is 1× Cerebras CS-3 (WSE-3) at 8423 tok/s.

Adjust the workload yourself

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

Open in calculator