What GPU do I need to run 01-ai/Yi-34B?
34.4B parameters, published in BF16. View on Hugging Face
Set up Yi-34BYi-34B is published by 01-ai on Hugging Face, with 10,429 downloads and 1,302 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 64.1 GB | 76.9 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX A5000 | 4 | $0.704/hr | ||
| FP8 (quantized) | 32.0 GB | 38.4 GB | L40 | 1 | $0.742/hr |
| cheaper alt. | RTX 4000 SFF Ada | 2 | $0.396/hr | ||
| INT4 (quantized) | 16.0 GB | 19.2 GB | RTX A5000 | 1 | $0.176/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Yi-34B at its published (BF16) precision: 1× A100, at $1.21/hr per GPU ($1.21/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Yi-34B: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 245,760 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.94 GB |
Computed from the layer, head and window counts in the model's own configuration (01-ai/Yi-34B), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Yi-34B: common questions
Can Yi-34B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 76.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
What is the least VRAM Yi-34B can run in?
19.2 GB, at INT4 (quantized), which fits a 24 GB card, against 76.9 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing Yi-34B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Yi-1 models
- Yi-34B-Chat (34.4B, BF16)
- Yi-6B-200K (6.1B, BF16)
- Yi-6B (6.1B, BF16)
- Yi-6B-Chat (6.1B, BF16)
Alternatives at this size
Other models for text-generation within about a third of Yi-34B's 34.4B parameters, from other model lines.
- Qwen3-32B (32.8B, BF16)
- GLM-4.7-Flash (31.2B, BF16)
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (31.6B, BF16)
- granite-4.1-30b (28.9B, BF16)
- Ornith-1.5-35B-A3B (36.0B, BF16)
More on Yi-34B
Fits on an 80 GB GPU at BF16: every model that fits in 80 GB.
Fits on a 48 GB GPU at FP8: every model that fits in 48 GB.
Fits on a 24 GB GPU at INT4: every model that fits in 24 GB.
Best chat and assistants models: how Yi-34B ranks against the rest.
Related reading: A100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
