Every open model in our directory, ranked by how well it runs on Google Cloud TPU v6e (Trillium) (32GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
Google Cloud TPU v6e (Trillium) has 32 GB of VRAM, enough to run 119 of the 169 open models we track at 4-bit. The largest that still fits well is minimax-m2.5 (230B), and the top-rated is minimax-m2.5 at about 58 tok/s.
119 of 169 models run comfortably on Google Cloud TPU v6e (Trillium).
See full Google Cloud TPU v6e (Trillium) specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| minimax-m2.5MiniMax | SS | 58.2 tok/s | 22.7 GB |
| Mixtral 8x7B InstructMistral AI | SS | 116.2 tok/s | 11.4 GB |
| Falcon 40B InstructTechnology Innovation Institute | SS | 54.2 tok/s | 24.4 GB |
| Qwen3.5-9BAlibaba | SS | 53.7 tok/s | 24.6 GB |
| Gemma 4 26B-A4B IT |
minimax-m2.5 is the highest-rated model that runs on Google Cloud TPU v6e (Trillium) at 4-bit, at about 58 tok/s. In total, Google Cloud TPU v6e (Trillium) can run 119 of the 169 open models we track.
We size each model at 4-bit (Q4_K_M), add the KV cache for its context length and a fixed runtime overhead, then compare that to this device VRAM. Each model is graded on how comfortably it fits and how fast it should run on this card.
It is an estimate of decode speed in tokens per second at 4-bit, based on this device memory bandwidth and each model size. Real speed depends on your runtime, batch size, and context length, so use it to compare models, not as a hard promise.
Lower-bit quantization shrinks a model so more of them fit. These grades already assume 4-bit, which is the common local default. Going below 4-bit can squeeze a larger model in at some quality cost; going above needs more VRAM and may not fit.
| SS |
| 119.9 tok/s |
| 11.0 GB |
| DiffusionGemma 26B-A4BGoogle | SS | 125.9 tok/s | 10.5 GB |
| Llama 3.1 8B InstructMeta | SS | 99.0 tok/s | 13.3 GB |
| Nemotron 3 Nano OmniNVIDIA | SS | 154.7 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | SS | 154.7 tok/s | 8.5 GB |
| Qwen3.5-35B-A3BAlibaba | SS | 154.7 tok/s | 8.5 GB |
| North Mini CodeCohere | SS | 157.5 tok/s | 8.4 GB |
| Llama 2 13B ChatMeta | SS | 155.9 tok/s | 8.5 GB |
minimax-m2.5MiniMax | 230B(10B active) | SS | 58.2 tok/s | 22.7 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | SS | 116.2 tok/s | 11.4 GB | |
Falcon 40B InstructTechnology Innovation Institute | 40B | SS | 54.2 tok/s | 24.4 GB | |
Qwen3.5-9BAlibaba | 9B | SS | 53.7 tok/s | 24.6 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | SS | 119.9 tok/s | 11.0 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | SS | 125.9 tok/s | 10.5 GB | |
| 8B | SS | 99.0 tok/s | 13.3 GB | ||
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | SS | 154.7 tok/s | 8.5 GB | |
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Qwen3.6 35B-A3BAlibaba | 35B(3B active) | SS | 154.7 tok/s | 8.5 GB | |
Qwen3.5-35B-A3BAlibaba | 35B(3B active) | SS | 154.7 tok/s | 8.5 GB | |
North Mini CodeCohere | 30B(3B active) | SS | 157.5 tok/s | 8.4 GB | |
Llama 2 13B ChatMeta | 13B | SS | 155.9 tok/s | 8.5 GB | |
Qwen3.5-122B-A10BAlibaba | 122B(10B active) | SS | 48.4 tok/s | 27.3 GB | |
Qwen3-30B-A3BAlibaba | 30B(3B active) | SS | 245.1 tok/s | 5.4 GB | |
| 9B | SS | 219.5 tok/s | 6.0 GB | ||
| 8B | AA | 233.1 tok/s | 5.7 GB | ||
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LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 454.3 tok/s | 2.9 GB | |
Gemma 4 E4B ITGoogle | 4B | AA | 190.9 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | AA | 190.9 tok/s | 6.9 GB | |
Mistral 7B InstructMistral AI | 7B | AA | 206.4 tok/s | 6.4 GB | |
PersonaPlex 7BNVIDIA | 7B | AA | 275.7 tok/s | 4.8 GB | |
Llama 2 7B ChatMeta | 7B | AA | 275.7 tok/s | 4.8 GB | |
VibeThinker-3BWeiboAI | 3B | AA | 346.2 tok/s | 3.8 GB | |
Gemma 4 E2B ITGoogle | 2B | AA | 356.0 tok/s | 3.7 GB | |
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Gemma 4 12BGoogle | 12B | BB | 41.2 tok/s | 32.0 GB | |
Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
Mixture-of-experts models only activate a fraction of their parameters at a time, so their runtime memory is closer to the active size than the headline size. We score fit on the active parameters, which is why some very large models still fit.
