How much VRAM does ERNIE 4.5 21B-A3B need?

About 14.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 15.7 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will ERNIE 4.5 21B-A3B run on a 16GB GPU?

Yes — at Q4_K_M and 4K context it needs about 14.1 GB, which leaves 1.9 GB spare on a RTX 4060 Ti 16G, or on any of the other 16 GB cards here (15 in all). That is the smallest size in this index that clears it comfortably; 45 of 70 do.

Which quantization of ERNIE 4.5 21B-A3B should I use?

No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

ERNIE 4.5 21B-A3B

21B-A3B

Baidu ERNIE 4.5

Baidu's open mixture-of-experts model: about 21.8B parameters in total and roughly 3B active per token. Each token goes through 6 of 64 routed experts plus 2 shared ones. The total decides the memory and the active count decides the speed, so it needs the VRAM of a 20B-class model and reads about as much per token as a 3B one. At Q4_K_M it needs about 14.1 GB at 4K context, which is right at the edge of comfortable on a 16 GB card and tight by 32K. A 24 GB card, or a 24 GB Mac through the GPU's share of unified memory, leaves real room for context. Sizes use the calculator's generic rates, since no GGUF file size could be checked, and no per-level quality loss has been published.

⬇ 19.2K HF downloads♥ 178 likesbaidu/ERNIE-4.5-21B-A3B-PT· stats from 10/3/2026

Consumer GPUMac / Apple Silicon

131K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

—

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	14.1 GB	—	—	Estimated	Calc HF
GGUF	Q8_0	8.5	24.4 GB	—	—	Estimated	Calc HF

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Running ERNIE 4.5 21B-A3B locally

At Q4_K_M and 4K of context, ERNIE 4.5 21B-A3B needs about 14.1 GB — 12.6 GB of weights, 0.22 GB of KV cache and a 1.3 GB activation buffer. The smallest cards in this index that clear that comfortably are 16 GB ones — 15 of them, from the RTX 4060 Ti 16G up — and 45 of the 70 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 1.53 GB, taking the total to 15.7 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 22 GB.

Which build to download

This index only tracks ERNIE 4.5 21B-A3B in GGUF, across 2 levels (Q4_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does ERNIE 4.5 21B-A3B need?: About 14.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 15.7 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will ERNIE 4.5 21B-A3B run on a 16GB GPU?: Yes — at Q4_K_M and 4K context it needs about 14.1 GB, which leaves 1.9 GB spare on a RTX 4060 Ti 16G, or on any of the other 16 GB cards here (15 in all). That is the smallest size in this index that clears it comfortably; 45 of 70 do.
Which quantization of ERNIE 4.5 21B-A3B should I use?: No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 4060 Ti 16G16GB · +1.9 Radeon RX 7600 XT16GB · +1.9 Radeon RX 9060 XT 16G16GB · +1.9 RTX 5060 Ti 16G16GB · +1.9 Radeon RX 6900 XT16GB · +1.9
Tight but possible: Mac M3 Pro 18G18GB
Just misses: RTX 3060 12G+2.1GB over Radeon RX 6700 XT+2.1GB over
Ships in: GGUF
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-10-03 RSS → /feed.xml