How much VRAM does Metis-1.6 Praxis need?

About 2.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 2.4 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 Metis-1.6 Praxis run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 2.1 GB, which leaves 3.9 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.

Which quantization of Metis-1.6 Praxis 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 5 levels in the index are IQ4_XS, Q4_K_M, Q5_K_M, Q6_K, 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.

Metis-1.6 Praxis

3B MoE

Metis

The smaller of Lernex's two Metis-1.6 models, released on 1 November under Apache 2.0. It is a looped hybrid: 12 layers, mostly Mamba-2 with two attention layers and a small mixture-of-experts block on every layer, and the whole stack runs twice per token, so each token reads about 1.1B parameters' worth of weights from a 2.95B model. Each pass keeps its own attention cache, about 8 KB per token in total, which makes its 128K window cheap. At Q4_K_M it needs about 2.2 GB at 4K and 3.2 GB at 128K, comfortable on a 4 GB card. Q8_0 needs about 4.5 GB at 128K, comfortable on a 6 GB card. llama.cpp runs it since 3 November. Sizes come from Lernex's own GGUF files, and no per-level quality loss has been published.

⬇ 284 HF downloads♥ 0 likesLernex/Metis-1.6-Praxis-GGUF· stats from 11/5/2026

Consumer GPUMac / Apple SiliconCPU / VPS

131K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

—

Perplexity rise (best build)

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	IQ4_XS	4.98	1.9 GB	—	—	Estimated	Calc HF
GGUF	Q4_K_M	5.48	2.1 GB	—	—	Estimated	Calc HF
GGUF	Q5_K_M	6.31	2.5 GB	—	—	Estimated	Calc HF
GGUF	Q6_K	7.04	2.8 GB	—	—	Estimated	Calc HF
GGUF	Q8_0	8.74	3.4 GB	—	—	Estimated	Calc HF

Which file each size is for

A file size is not a memory figure: the VRAM column is the calculator's total at 4K context — weights plus KV cache plus a 10% activation buffer. Units differ on purpose: file sizes are decimal GB, as Hugging Face lists them; the VRAM column is GiB (1024³ bytes) with 2% added to the weights, so 7.12 GB of file counts as about 6.8 GiB there. A vision projector (mmproj), where the repo ships one, is a separate download and is in neither number.

GGUF IQ4_XSLernex/Metis-1.6-Praxis-GGUF · Metis-1.6-Praxis-IQ4_XS.gguf · 1.84 GB (1,836,154,464 bytes) · listing read 2026-11-04
GGUF Q4_K_MLernex/Metis-1.6-Praxis-GGUF · Metis-1.6-Praxis-Q4_K_M.gguf · 2.02 GB (2,019,139,168 bytes) · listing read 2026-11-04
GGUF Q5_K_MLernex/Metis-1.6-Praxis-GGUF · Metis-1.6-Praxis-Q5_K_M.gguf · 2.33 GB (2,325,706,336 bytes) · listing read 2026-11-04
GGUF Q6_KLernex/Metis-1.6-Praxis-GGUF · Metis-1.6-Praxis-Q6_K.gguf · 2.60 GB (2,597,131,872 bytes) · listing read 2026-11-04
GGUF Q8_0Lernex/Metis-1.6-Praxis-GGUF · Metis-1.6-Praxis-Q8_0.gguf · 3.22 GB (3,224,393,312 bytes) · listing read 2026-11-04

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The larger of Lernex's two Metis-1.6 models, released on 1 November under Apache 2.0. It is a looped hybrid: 20 layers, mostly Mamba-2 with three attention layers and a mixture-of-experts block on every layer, and the whole stack runs twice per token, so each token reads about 2.6B parameters' worth of weights from a 10.24B model. Each pass keeps its own attention cache, about 15 KB per token in total. IQ4_XS needs about 6.4 GB at 4K and 6.9 GB at 32K, comfortable on an 8 GB card. Q4_K_M needs about 7.1 GB at 4K, tight on an 8 GB card and comfortable from 10 GB up; at 128K it needs about 9.1 GB, comfortable on a 12 GB card. Q8_0 needs about 11.7 GB at 4K, comfortable on a 16 GB card. llama.cpp runs it since 3 November. Sizes come from Lernex's own GGUF files, and no per-level quality loss has been published.

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Running Metis-1.6 Praxis locally

At Q4_K_M and 4K of context, Metis-1.6 Praxis needs about 2.1 GB — 1.9 GB of weights, 0.03 GB of KV cache and a 0.2 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 11 of them, from the Mac M1 8G up — and 78 of the 78 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 0.22 GB, taking the total to 2.4 GB. That is gentler than the parameter count suggests: only 4 of 12 layers keep a KV cache that grows with context, so the cache scales at a fraction of the usual rate. The model's native window is 128K; holding all of it at Q4_K_M would need about 3 GB.

Which build to download

This index only tracks Metis-1.6 Praxis in GGUF, across 5 levels (IQ4_XS, Q4_K_M, Q5_K_M, Q6_K, 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 Metis-1.6 Praxis need?: About 2.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 2.4 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 Metis-1.6 Praxis run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 2.1 GB, which leaves 3.9 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.
Which quantization of Metis-1.6 Praxis 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 5 levels in the index are IQ4_XS, Q4_K_M, Q5_K_M, Q6_K, 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: Mac M1 8G8GB · +3.9 Mac M3 8G8GB · +3.9 RTX 40608GB · +5.9 RTX 4060 Ti 8G8GB · +5.9 Radeon RX 9060 XT 8G8GB · +5.9
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 2026-11-05 RSS → /feed.xml