Ling 3.1 Flash 1019 — Hardware Requirements & GPU Compatibility
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Ling-3.1-flash-1019 is an October 19 checkpoint update of Ling-3.1-flash from inclusionAI, the Ant Group research team. It is a sparse hybrid-linear Mixture-of-Experts model with about 560 billion total parameters and 25 billion active per token, using 512 routed experts with 8 active, and layers split 7:1 between KDA linear attention and gated latent attention. Further post-training targets reasoning and real-world agentic work, and inclusionAI reports gains on AutomationBench-AA, CritPt, and SciCode over its predecessor. All experts must still fit in memory even though only a fraction is active. It ships with a 262,144-token context window, which inclusionAI says extends to 1 million tokens. It is released under the MIT license, which permits commercial use, and was published in October 2026. The reference deployment uses eight H200 GPUs, so local use means a multi-GPU server or a very large unified-memory machine even at 4-bit.
Ling 3.1 Flash 1019 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
Macs from 192 GB (IQ2_S · ~35 tok/s)
MIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Ling 3.1 Flash 1019
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve inclusionAI/Ling-3.1-flash-1019 --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: Ling-3.1-flash-1019-Q4_K_M/Ling-3.1-flash-1019-Q4_K_M-00001-of-00010.gguf357.97 GB + 9 partsbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Rent 5× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$11.35/hr
- est., whole server
- GPUs
- 5 GPUs
- 400 GB of VRAM
How Much VRAM Does Ling 3.1 Flash 1019 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 245.9 GB | 246.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on Instinct MI325X 256 GB | |
| 253.0 GB | 253.3 GB | 3-bit small — 3-bit small quantization | Runs on Instinct MI325X 256 GB | |
| 281.6 GB | 281.9 GB | 3-bit medium — 3-bit medium quantization | Runs on Instinct MI350X 288 GB | |
| 288.8 GB | 289.1 GB | 4-bit · legacy — 4-bit legacy quantization | Runs on Instinct MI350X 288 GB | |
| 346.0 GB | 346.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 5× H100 SXM 80 GB | |
| 410.4 GB | 410.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 6× H100 SXM 80 GB | |
| 474.7 GB | 475 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 7× H100 SXM 80 GB | |
| 574.8 GB | 575.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 8× H100 SXM 80 GB |
Selected Q4_K_M
4-bit medium quantization344.9GBat 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 1019?
Q4_K_M · 346.0 GBLing 3.1 Flash 1019 (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. 1 device with unified memory can run Ling 3.1 Flash 1019, including Mac Studio (M3 Ultra, 512GB).
192+ GB
Q4_K_M · best fit IQ2_S (180.4 GB)
6 GPUs · 1 Mac class · 2 device families
B200~182Simulate output on B200B300~182Simulate output on B300Instinct MI350X~62Simulate output on Instinct MI350XM3 Ultra256–512 GB~35Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~226Simulate output on NVIDIA DGX H1008x A100 SXM4~212Simulate output on NVIDIA DGX A100 640GB
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 1019
Ling family · 5 models →Similar models
3| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Step 5 PreviewStepFun | 128.0 | 407.5 GB | —needs > 24 GB | same size |
| NVIDIA Nemotron 3 Ultra 550B A55B BF16NVIDIA | 109.6 | 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
- 2026-10-27
- License
- MIT
Frequently Asked Questions
How much VRAM does Ling 3.1 Flash 1019 need?
Ling 3.1 Flash 1019 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
Can NVIDIA GeForce RTX 5090 run Ling 3.1 Flash 1019?
No — Ling 3.1 Flash 1019 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 1019?
For Ling 3.1 Flash 1019, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Ling 3.1 Flash 1019 on a Mac?
Yes — Mac Pro M2 Ultra (192 GB) and 3 other Macs can run Ling 3.1 Flash 1019, the smallest at IQ2_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 346.0 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 512GB).
Can I run Ling 3.1 Flash 1019 locally?
Yes — Ling 3.1 Flash 1019 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 1019?
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 1019?
No single consumer GPU has enough VRAM to run Ling 3.1 Flash 1019 at Q4_K_M (346.0 GB). Multi-GPU or professional hardware is required.
Which devices can run Ling 3.1 Flash 1019?
1 device with unified memory can run Ling 3.1 Flash 1019 at Q4_K_M (346.0 GB), including Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.