Google95 GB VRAM819 GB/s$4.2/hr

Llama 3.3 70B on TPU v5p (per-chip, 95GB): Does It Fit, How Fast, What It Costs

Llama 3.3 70B at FP8 (E4M3) needs 70.6 GB for its weights and 92.1 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
92.1 GB needed / 95 GB available
Decode speed
11.31
tok/s per stream
Aggregate
135.7
tok/s @ 8 concurrent
Cost / 1M tokens
$8.60
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 GB92.1 GB11.31135.75.16 s$8.60✓
2× TPU190 GB92.1 GB19.23461.52.60 s$5.06✓
4× TPU380 GB92.1 GB38.4618461.32 s$2.53✓
8× TPU760 GB92.1 GB76.917384673.0 ms$1.26✓
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?
4K181.34 GB✓ fits
8K92.68 GB✓ fits
33K210.74 GB✓ fits
66K121.47 GB✓ fits
131K042.95 GB✗ does not fit
Other GPUs that run Llama 3.3 70B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×4039$0.1376
A100 80GB SXM42×1127$0.5916
H100 SXM5 80GB2×1888$0.7358
AMD MI300X 192GB1×878.3$0.7906
L40S 48GB2×486.8$0.7988
AMD MI325X 288GB1×994.3$0.9778
Frequently asked questions

Can Llama 3.3 70B run on TPU v5p (per-chip, 95GB)?

Yes. Llama 3.3 70B at FP8 (E4M3) needs 92.1 GB including the KV cache, and TPU v5p (per-chip, 95GB) has 95 GB — so it fits on a single GPU.

How fast is Llama 3.3 70B on TPU v5p (per-chip, 95GB)?

11.31 tokens/sec per stream and 135.7 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 5.16 s, and inter-token latency is 88.4 ms.

What does Llama 3.3 70B on TPU v5p (per-chip, 95GB) cost per million tokens?

$8.60 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 Llama 3.3 70B need on TPU v5p (per-chip, 95GB)?

70.6 GB for the weights at FP8 (E4M3), plus 21.5 GB of KV cache for 8 concurrent requests at 8K context — 92.1 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is TPU v5p (per-chip, 95GB) the right choice for Llama 3.3 70B?

It works. Compare $8.60 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.1031, and the fastest is 4× Cerebras CS-3 (WSE-3) at 45076 tok/s.

Adjust the workload yourself

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

Open in calculator