Qwen 3.8 27B — October 2026
Community sentiment from 10 posts · 17 comments · collected November 10, 2026 · generated November 10, 2026
Qwen 3.8 27B — October 2026 scores 62.86/100 overall in the r/LocalLLaMA community benchmark, collected November 10, 2026 from 10 posts and 17 comments. Its strongest category is Intelligence at 70/100, and its weakest is Multimodal & vision at 55/100.
Confidence: medium — Direct mentions are a thin slice of the 10 top posts' comment volume, and much of them is jokes, setup posts, and spec-decoding tooling discussion rather than capability reviews, so only three of ten categories have direct user data; the positive intelligence claim rests on a single high-score comment.
Category scores
Intelligence
70Positive. The most-upvoted direct take (46 points) argues 'Qwen 27B is good because (big) Qwen 3.8 is better than GPT 4o' and credits the distillation for keeping the big model's capability compressible. Other direct mentions are jokes ('labotomized with an IQ of 2') or neutral benchmarking comparisons, so the positive signal is real but thin.
Coding quality
no dataNo data in the sample — no direct user statements about the model's code-generation quality. Coding appears only as a variable in spec-decoding speedup discussion and as a tok/s benchmark in one setup post.
Instruction following
no dataNo data in the sample — no direct user statements about how well the model follows instructions.
Long context
no dataNo data in the sample — context shows up only as users' own setup parameters (120,000-token context, 512k for a fine-tune) and as a caveat that speed depends on context size, not as statements about the model's long-context capability.
Price / value
no dataNo data in the sample — no direct statements about price or cost-effectiveness. Quant mentions (Q4_K_M, fp8, FP16) are about precision, not cost.
Speed
60Mixed. Positive: one user reports 'Realistically around 60tps decode and ~1100 prefill with Q4_K_M' and another reports 74.4–90.2 tok/s prose and 110.6–130.5 tok/s code with DFlash2 on a 3090-class setup. Negative: one user calls the model 'notorious for excessive thinking' (heavy reasoning), and both users note speeds drop as the context window fills. The thread also fills with absurd speed jokes (a million t/s on a toaster), which signal skepticism about claimed speed.
Reliability & uptime
no dataNo data in the sample — no statements about reliability, stability, or uptime.
Creativity & writing
no dataNo data in the sample — no direct user statements about the model's writing or creative quality.
Multimodal & vision
55Split. One user runs mmproj on the GPU with a BF16 projector and is satisfied, and a setup post reports a CPU-only BF16 vision encoder that 'works perfectly for my use, including image understanding and OCR'. Against that, the thread's most experienced local runner objects: 'I just don't like the CPU vision part. It is simply way too slow for me, and GPU vision is a must.'
Agentic & tool use
no dataNo data in the sample — no direct user statements about agentic behavior or tool use.
Summary
In the 10 highest-scoring October 2026 localllama posts, Qwen 3.8 27B is mentioned directly in a small minority of comments, and most of that mention volume is wrapped in jokes, hardware-setup posts, and spec-decoding tooling discussion rather than capability reviews. The model mainly shows up as the anchor of the Qwen 3.8 family in benchmark comparisons and as the base for derivatives.
Where users make direct quality claims, they lean positive. The most-upvoted direct take (46 points) frames the 27B as a distillation that keeps what makes big Qwen 3.8 'better than GPT 4o', and hands-on 3090 users report roughly 60tps decode and ~1100 prefill at Q4_K_M, with one DFlash2 setup reaching 74.4–90.2 tok/s on prose and 110.6–130.5 tok/s on code at 120,000-token context.
The sharpest criticisms concern efficiency, not intelligence. One user calls Qwen 3.8 27b 'notorious for excessive thinking', and the thread's most experienced local runner says the CPU vision path is 'way too slow', making GPU vision 'a must'. The model also gets mocked in-thread ('labotomized with an IQ of 2'), and one user is evaluating a Swift 1.5 QFN fine-tune with up to 512k context and may not return to Qwen 27B.
Ecosystem context dominates the rest of the sample: Qwen3.8-27B is the base for a quant-aware-retrained derivative line (Bonsai 2), a spec-decoding target (MTP, Eagle-4, DFlash2), and the comparison anchor in benchmark threads about smaller models. Per one user, DFlash2 'tops out around 2.5 accepted tokens per step on prose'. Net sentiment from direct statements: modestly positive on raw capability and local speed, negative on reasoning overhead and the slow CPU vision path.
Pros and cons
Pros
- Positive intelligence take: the top direct comment (46 points) says the 27B distillation keeps what makes big Qwen 3.8 'better than GPT 4o'.
- Reported local speed is good: ~60tps decode and ~1100 prefill at Q4_K_M on one 3090-class setup; another reports 74.4–90.2 tok/s prose and 110.6–130.5 tok/s code with DFlash2.
- Fits a single RTX 3090: users run it at 120,000-token context with INT8 KV cache in a 17.68 GiB mixed-precision package.
- Vision is usable on GPU: mmproj-BF16 runs alongside the GPU language model, and one CPU-only BF16 vision build 'works perfectly' for image understanding and OCR.
- Active optimization ecosystem: MTP, Eagle-4 and DFlash2 spec decoding, quant-aware-retrained derivatives, and community-optimized packages (WarlaxZ, beellama configs).
Cons
- 'Notorious for excessive thinking' — a direct complaint about heavy, token-expensive reasoning.
- CPU vision path is 'way too slow'; GPU vision is 'a must' according to the thread's most experienced runner.
- Speed is context-dependent: decode and prefill 'progressively slow down as the context window fills up'.
- Faces substitution by fine-tunes: one user is evaluating Swift 1.5 QFN (up to 512k context) and is not sure they will return to Qwen 27B.
- Thin, partly sarcastic direct signal: much of the mention volume is jokes ('a million t/s on a toaster', 'labotomized with an IQ of 2') rather than substantive capability reviews.
Top quotes
“Qwen 27B is good because (big) Qwen 3.8 is better than GPT 4o.”
alfredr · 46 points positive view on Reddit ↗ “You can make Qwen 27b run at a million t/s on a toaster if you quant it hard enough.”
Cerevox · 79 points neutral view on Reddit ↗ “I run Qwen 27b at 1000t/s on my Samsung smart fridge clustered with a Vorwerk Thermomix at Q0.001”
jensilo · 16 points neutral view on Reddit ↗ “qwen 3.8 27B labotomized with an IQ of 2”
pizza_with_no_cheese · 2 points negative view on Reddit ↗ “Qwen 3.8 27b is notorious for excessive thinking.”
Gohab2001 · 3 points negative view on Reddit ↗ “I think comparing this model to qwen 3.8 27B FP16 is a miss, they should be comparing it to similar GB size models”
Practical-Collar3063 · 47 points neutral view on Reddit ↗ “For me (club-3090) still nothing beats Qwen3.8-27B+MTP, and I have tried a lot!”
caetydid · 5 points positive view on Reddit ↗ “Realistically around 60tps decode and \~1100 prefill with Q4\_K\_M. Prefill is quite stable with beellama, but decode depends a lot on the current task.”
caetydid · 2 points positive view on Reddit ↗ “Prose generation: 74.4–90.2 tok/s”
AdonosFlew · 1 points positive view on Reddit ↗ “I just don't like the CPU vision part. It is simply way too slow for me, and GPU vision is a must.”
caetydid · 1 points negative view on Reddit ↗ “Anyone tried Eagle-4 on Qwen3.8-27B yet? DFlash2 tops out around 2.5 accepted tokens per step on prose for me, curious if Eagle-4 does better there.”
Ishannaik · 1 points neutral view on Reddit ↗
Top sample posts
Top 5 of 10 posts by score
- I made my iPhone a second GPU for my 24 GB MacBook: Qwen 3.8 27B prefills 29–44% faster & my holds part of the CTX window. ↗
StayLameBro · 2331 points · 333 comments
- How is it possible that qwen 27b is so good? When GPT 4o had a trillion parameters and was worse? ↗
SignificantZebra5883 · 1706 points · 395 comments
- Someone is running Qwen 27B at ~20 tok/s on a jailbroken PS5's GPU ↗
LatentSpacer · 1130 points · 146 comments
- Qwen3.8-27B-Humanlike-Chat 2.0: texts like a human, now with tool calls and better instruction following ↗
kvyb · 941 points · 192 comments
- Someone jailbroke a PS5 and got Qwen 3.8 27B running on its GPU at 20 tok/s. ↗
98Saman · 533 points · 68 comments
FAQ
- What is Qwen 3.8 27B — October 2026's overall community score?
- 62.86 out of 100, with medium confidence: Direct mentions are a thin slice of the 10 top posts' comment volume, and much of them is jokes, setup posts, and spec-decoding tooling discussion rather than capability reviews, so only three of ten categories have direct user data; the positive intelligence claim rests on a single high-score comment.
- How was the Qwen 3.8 27B — October 2026 benchmark collected?
- From 10 posts and 17 comments on r/LocalLLaMA sampled over the past month window.
- Which categories does the Qwen 3.8 27B — October 2026 report score?
- Intelligence, Coding quality, Instruction following, Long context, Price / value, Speed, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.
- What are the main community pros and cons for Qwen 3.8 27B — October 2026?
- Top pros: Positive intelligence take: the top direct comment (46 points) says the 27B distillation keeps what makes big Qwen 3.8 'better than GPT 4o'.; Reported local speed is good: ~60tps decode and ~1100 prefill at Q4_K_M on one 3090-class setup; another reports 74.4–90.2 tok/s prose and 110.6–130.5 tok/s code with DFlash2.. Main cons: 'Notorious for excessive thinking' — a direct complaint about heavy, token-expensive reasoning.; CPU vision path is 'way too slow'; GPU vision is 'a must' according to the thread's most experienced runner..
- When was the Qwen 3.8 27B — October 2026 data collected?
- Collected on November 10, 2026; the report was generated on November 10, 2026.