qwen3.8

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Run Qwen3.8 Flash Next locally: the 8GB, 12GB, 16GB and 24GB setups that work (Nov 2026)

A 125B model on a gaming card sounds wrong. Flash Next uses only 6B parameters per token, so the experts can live in system RAM and the n-gram table on your SSD. What each card size needs, which quants people run, and where the quality argument stands.

Aliteq Team · Nov 1, 2026 · 11 min

Line-art illustration on a dark background next to the headline: One LM Studio switch Put MoE expert weights on the CPU. A card reads Setting: MoE on CPU (LM Studio).

LM Studio's 'force MoE weights onto CPU' slider: fit a 30B model or Qwen3.8 Flash Next

It started as a toggle and became a layer slider in LM Studio 0.4.0. Where it lives, what it moves, the number to set on an 8, 12 or 16GB card, whether LM Studio runs Qwen3.8 Flash Next, and why Mac MLX models don't have the setting.

Aliteq Team · Updated Nov 1, 2026 · 8 min

Line-art illustration on a dark background next to the headline: Ollama and MoE No --n-cpu-moe. Here is what it does. A card reads Common mistake: Defaults (context + quant).

Ollama has no --n-cpu-moe switch. What it does with MoE experts, and Qwen3.8 Flash Next

The GitHub request to keep MoE experts in RAM is still open after 15 months. But since Ollama 0.30, llama.cpp's own fit step can park them there for you, as long as you leave num_gpu alone. Plus what Ollama's Qwen3.8 Flash Next tags need, and why Reddit can tell when you're running Ollama.

Aliteq Team · Updated Nov 1, 2026 · 8 min

Line-art illustration on a dark background next to the headline: Qwen3.8 27B needs VRAM by quant, and the 16GB squeeze. A card reads Reddit pick: 10-16GB #1 (r/LocalLLaMA).

Qwen3.8 27B Hardware Requirements: VRAM by Quant, How 16 GB Owners Run It, and Why It Can Disappoint

A 24 GB card runs Qwen3.8 27B at Q4 or Q5, and 32 GB runs Q6 with room to spare. 16 GB works too, at a 3-bit quant with a squeezed cache. Here is the math, what Reddit owners report, and what to check when it codes badly.

Aliteq Team · Updated Nov 1, 2026 · 12 min

Headline: 12GB or 16GB card? Buy the RAM. A screenshot of the ISTA-DASLab Hugging Face page shows which Qwen3.8 Flash Next shard must be resident and which can stay on disk. A card reads: Qwen3.8 Flash Next IQ2_XS, 48 GB RAM, Strata docs, read Nov 3, 2026.

Which MoE model runs best on a 12GB or 16GB GPU, and how much RAM do you need?

On a 12GB or 16GB card, your system RAM decides which mixture-of-experts model you can run. Nine files from gpt-oss 20B to Qwen3.8 Flash Next, split into what stays on the card and what moves to RAM, with the --n-cpu-moe starting value for each. Re-checked Nov 3, 2026.

Aliteq Team · Updated Nov 3, 2026 · 11 min

Line-art illustration on a dark background next to the headline: Still worth the 35B? Qwen3.6 35B-A3B vs the 3.8 generation. A card reads Active per token: 3B (fast on 16GB).

Qwen3.6 35B-A3B in late 2026: who should still run it, and when to switch to Qwen3.8

The fast 35B MoE that thinks like a 3B is now last generation. It still makes sense for speed on a 16 to 24 GB card and for its coder fine-tunes. For the best answers, Reddit owners are moving to Qwen3.8 27B or Flash Next.

Aliteq Team · Updated Nov 1, 2026 · 8 min

AMD's Radeon AI PRO R9700 workstation graphics card

AMD just proved you don't need a graphics card to run a serious AI model at home

Day-zero support for the new Qwen3.8 27B just landed on two AMD boxes — one needs a graphics card, one doesn't — and the tokens-per-second numbers are real.

Aliteq Team · Updated Oct 27, 2026 · 6 min

Line drawing of a graphics card next to the headline What fits 8GB? Local AI models that run on an RTX 4060 or 5060, with a card reading Qwen3.5 9B, Q4_K_M, 32K context, 7.2 GB, our VRAM engine, Nov 3, 2026

Which local AI models fit on an 8GB GPU? Qwen3.5 9B, Gemma 4 and what doesn't

Qwen3.5 9B and Gemma 4 E4B fit an 8GB card with room for real context. Gemma 4 12B only just fits. Qwen3.8 27B and gpt-oss-20b don't, and Qwen3.8 Flash Next needs 48GB or more of system RAM. Every number below, model by model, re-checked Nov 3, 2026.

Aliteq Team · Updated Nov 3, 2026 · 8 min

Night line-art illustration next to the headline: The 12GB pick changed RTX 3060 and 4070: what runs now. A card reads New 12GB pick: Gemma 4 12B (Q6_K · our VRAM engine).

12GB GPU? The best local LLM for an RTX 3060 or 4070, updated for November 2026

Gemma 4 12B at Q6_K is the 12GB pick that still leaves room for long chats. Qwen3.8 27B can squeeze in at a 3-bit quant with short context, and bigger MoE models run if your system RAM is large. Here is what fits, on our numbers.

Aliteq Team · Updated Nov 1, 2026 · 8 min

Line-art illustration on a dark background next to the headline: Run Qwen at home Qwen3.8 27B or Flash Next: which one fits. A card reads Native context: 262K tokens (Qwen model card).

How to run Qwen locally (Nov 2026): Qwen3.8 27B, Flash Next, and the Ollama defaults that make it look bad

Qwen3.8 27B is the default pick for a 16 to 24GB card, and Flash Next runs on smaller cards with lots of RAM. One command gets you started. Three default settings decide whether it is any good.

Aliteq Team · Updated Nov 1, 2026 · 9 min

Night line-art illustration next to the headline: 16GB just got bigger From gpt-oss-20b to Qwen3.8 27B. A card reads gpt-oss-20b on 16GB: 32K context (our VRAM engine).

16GB GPU? Which local AI models fit now, from gpt-oss-20b to Qwen3.8 27B (Nov 2026)

16GB used to stop at 14B models. Now gpt-oss-20b fits with 32K of context, and Qwen3.8 27B fits at a 3-bit quant. Here is what runs on an RTX 5060 Ti, 4060 Ti or 5070 Ti, on our numbers, and when system RAM takes you further.

Aliteq Team · Updated Nov 1, 2026 · 8 min

What is the best coding model you can run on one GPU? On 24GB, Qwen3.8 27B

The short answer for a 3090, 4090 or any 24GB card, what changes on 16GB and 12GB, the fast MoE alternatives, and the honest limits of every benchmark number, re-checked on each vendor's own page Nov 6, 2026.

Aliteq Team · Updated Nov 6, 2026 · 9 min

Line-art illustration on a dark background next to the headline: The MoE offload trick What --n-cpu-moe really does. A card reads The real limit: RAM bandwidth (not your GPU).

llama.cpp --n-cpu-moe: what it does, why more offload is slower, and Qwen3.8 Flash Next

Moving MoE experts into system RAM can make a model five times faster, or noticeably slower. Both are true. With Qwen3.8 Flash Next now running on 8GB and 12GB cards, here is what the flag moves, where your RAM becomes the limit, and which speed tricks quietly change the answers.

Aliteq Team · Updated Nov 1, 2026 · 12 min