Inclusion AILingBailingMoeV3ForCausalLM

Ling 3.1 Flash — Hardware Requirements & GPU Compatibility

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Ling-3.1-flash is a hybrid reasoning model from inclusionAI, the Ant Group research team, and an upgrade over Ling-3.0-flash. It is a sparse mixture-of-experts model with about 560 billion total parameters and 25 billion active per token, using a hybrid-linear design with mostly linear-attention layers and a few gated latent-attention layers. The card targets general agents, search, office tasks, and software engineering, and reports results on Terminal-Bench, SWE-bench Pro, and MCP-Atlas. The reference deployment uses eight H200 GPUs, so local use means a server or a very large unified-memory machine even at 4-bit. The card states a 1 million token context window. It is released under the MIT license, which permits commercial use. The model supports hybrid reasoning, switching between longer thinking and direct answers, and works with agent harnesses including Claude Code, Qwen Code, and Kilo Code. SGLang is the recommended serving stack, with multi-token prediction for faster decoding.

572.1B params · MoE25B active262K contextMITOct 20261.3K downloads113 likes1.4K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Ling 3.1 Flash runs on a 512 GB+ Mac or a multi-GPU server

Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 344.9 GB · ~24 tok/s

MIT

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Ling 3.1 Flash

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Other ways

each opens step-by-step

Run in cloud

Rent 5× H100 SXM · Q4_K_M

Estimated hourly price: ~$10.81/hr; est., whole server
GPUs: 5 GPUs; 400 GB of VRAM

Deployon Vast.aiRent H100 SXM$2.16/hr per GPU

Compare GPUs →

How Much VRAM Does Ling 3.1 Flash Need?

Select a quantization level; the listed hardware adheres to it.

VRAM	+32K	+128K	File	Quality	Runs on
160.1 GB	160.4 GB	168.5 GB	174.1 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Runs on B200 192 GB
174.3 GB	174.7 GB	182.8 GB	198.2 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Runs on B200 192 GB
181.5 GB	181.8 GB	190 GB	188.5 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Runs on B200 192 GB
195.8 GB	196.1 GB	204.3 GB	218.1 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Runs on Instinct MI325X 256 GB
224.4 GB	224.7 GB	232.9 GB	256.4 GB	3-bit · i-quant — Importance-weighted 3-bit	Runs on Instinct MI325X 256 GB
238.7 GB	239 GB	247.2 GB	260.4 GB	3-bit · i-quant extra small — Importance-weighted 3-bit, extra small	Runs on Instinct MI325X 256 GB
245.9 GB	246.2 GB	254.3 GB	219.2 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Runs on Instinct MI325X 256 GB
253.0 GB	253.3 GB	261.5 GB	261.6 GB	3-bit small — 3-bit small quantization	Runs on Instinct MI325X 256 GB
260.2 GB	260.5 GB	268.6 GB	277.1 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Runs on Instinct MI350X 288 GB
281.6 GB	281.9 GB	290.1 GB	275.1 GB	3-bit medium — 3-bit medium quantization	Runs on Instinct MI350X 288 GB
288.8 GB	289.1 GB	297.2 GB	326.6 GB	4-bit · legacy — 4-bit legacy quantization	Runs on Instinct MI350X 288 GB
295.9 GB	296.2 GB	304.4 GB	288 GB	3-bit large — 3-bit large quantization	Runs on 5× H100 SXM 80 GB
310.2 GB	310.5 GB	318.7 GB	337.8 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Runs on 5× H100 SXM 80 GB
324.5 GB	324.8 GB	333 GB	360.1 GB	4-bit · legacy+ — 4-bit legacy quantization with offset	Runs on 5× H100 SXM 80 GB
324.5 GB	324.8 GB	333 GB	336.6 GB	4-bit small — 4-bit small quantization	Runs on 5× H100 SXM 80 GB
324.5 GB	324.8 GB	333 GB	358 GB	4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear	Runs on 5× H100 SXM 80 GB
346.0 GB	346.3 GB	354.5 GB	354.4 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Runs on 5× H100 SXM 80 GB
396.1 GB	396.4 GB	404.5 GB	406.1 GB	5-bit small — 5-bit small quantization	Runs on 6× H100 SXM 80 GB
410.4 GB	410.7 GB	418.8 GB	400.2 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Runs on 6× H100 SXM 80 GB
474.7 GB	475 GB	483.2 GB	478 GB	6-bit, very good — 6-bit quantization, very good quality	Runs on 7× H100 SXM 80 GB
574.8 GB	575.1 GB	583.3 GB	600.8 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Runs on 8× H100 SXM 80 GB
1147.0 GB	1147.3 GB	1155.4 GB	~1144.2 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Runs on 7× B200 192 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

344.9GBat 16K context

Weights: 343.3 GB
KV cache: 1.4 GB
Overhead: 0.3 GB

Server-class: ~344.9 GB at 16K context · about 5× H100 SXM 80 GB

Which Hardware Can Run Ling 3.1 Flash?

Q4_K_M · 346.0 GB

Ling 3.1 Flash (Q4_K_M) requires 346.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 450+ GB is recommended. Using the full 262K context window can add up to 19.3 GB, bringing total usage to 365.3 GB. No consumer GPU has enough memory. 3 devices with unified memory can run Ling 3.1 Flash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 512GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Consumer GPUs (up to 32 GB) and Macs up to 128 GB: too small for this model (needs ≥ 159 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →

Alternatives to Ling 3.1 Flash

Ling family · 4 models →

Similar models

Model	Score	VRAM	tok/s	Why it is similar
NVIDIA Nemotron 3 Ultra 550B A55B BF16NVIDIA	111.0	378.4 GB	—needs > 24 GB	same size
LongCat Flash ChatMeituan	—	1244.5 GB	—needs > 24 GB	same size

Specifications

Publisher: Inclusion AI
Family: Ling
Parameters: 572.1B
Active parameters: 25.1B per token
Architecture: BailingMoeV3ForCausalLM
Context Length: 262,144 tokens
Vocabulary Size: 157,184
Release Date: 2026-10-13
License: MIT

Resources

Frequently Asked Questions

How much VRAM does Ling 3.1 Flash need?

Ling 3.1 Flash requires 346.0 GB of VRAM at Q4_K_M, or 1147.0 GB at BF16. Full 262K context adds up to 19.3 GB (365.3 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 572.1B × 4.8 bits ÷ 8 = 343.3 GB

KV Cache + Overhead ≈ 2.7 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 22 GB (at full 262K context)

VRAM usage by quantization

Q4_K_M

346.0 GB

Q4_K_M + full context

365.3 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Ling 3.1 Flash?

No — Ling 3.1 Flash requires at least 160.1 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Ling 3.1 Flash?

For Ling 3.1 Flash, Q4_K_M (346.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (396.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 160.1 GB.

VRAM requirement by quantization

IQ2_XXS

160.1 GB~53%

IQ3_XS

238.7 GB~73%

Q3_K_L

295.9 GB~86%

Q4_K_M ★

346.0 GB~89%

Q5_K_S

396.1 GB~92%

BF16

1147.0 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Ling 3.1 Flash on a Mac?

Yes, but only at lower quantizations. The smallest Mac that can run Ling 3.1 Flash is Mac Pro M2 Ultra (192 GB) at IQ2_XXS; 4 of the 156 Macs we list can run it at some quantization. For Q4_K_M (346.0 GB) you need a Mac with more unified memory.

Can I run Ling 3.1 Flash locally?

Yes — Ling 3.1 Flash can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

What's the download size of Ling 3.1 Flash?

At Q4_K_M, the download is about 343.27 GB. The full-precision BF16 version is 1144.24 GB. The smallest option (IQ2_XXS) is 157.33 GB.

Which GPUs can run Ling 3.1 Flash?

No single consumer GPU has enough VRAM to run Ling 3.1 Flash at Q4_K_M (346.0 GB). Multi-GPU or professional hardware is required.

Which devices can run Ling 3.1 Flash?

3 devices with unified memory can run Ling 3.1 Flash at Q4_K_M (346.0 GB), including Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.