Qwen3.8 27B SLQ EAR99 — Hardware Requirements & GPU Compatibility
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Qwen3.8 27B SLQ EAR99 is a 9.2B-parameter open language model from kaitchup in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 6.28 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~104 tok/s · 256K)Macs from 16 GB (Q4_K_M · ~22 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.8 27B SLQ EAR99
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 kaitchup/Qwen3.8-27B-SLQ-EAR99 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~94 tok/s
- generation speed
- Cost per 1M output tokens
- $0.36
- per 1M output tokens
How Much VRAM Does Qwen3.8 27B SLQ EAR99 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.7 GB | 6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~141 tok/s | |
| 5.2 GB | 6.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~125 tok/s | |
| 6.3 GB | 7.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~104 tok/s | |
| 7.3 GB | 8.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~90 tok/s | |
| 8.3 GB | 9.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~79 tok/s | |
| 10.0 GB | 11.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~66 tok/s | |
| 19.2 GB | 20.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 64K context · ~34 tok/s |
Selected Q4_K_M
4-bit medium quantization6.7GBat 16K context
- Weights
- 5.5 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.3 GB free · ~104 tok/s
≥50 tok/s needs ≥483 GB/s · RTX 5090 ~186, RTX 3090 Ti ~104
Which Hardware Can Run Qwen3.8 27B SLQ EAR99?
Q4_K_M · 6.3 GBQwen3.8 27B SLQ EAR99 (Q4_K_M) requires 6.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 262K context window can add up to 13.9 GB, bringing total usage to 20.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Qwen3.8 27B SLQ EAR99, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.3 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~79Simulate output on RTX 3080RTX 2080 Ti~64Simulate output on RTX 2080 TiRTX 3070 Ti~63Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.3 GB free · up to 128K ctx
1 GPU
RX 7900 XT~76Simulate output on RX 7900 XT32–40 GB
fits · 25.3 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~186Simulate output on RTX 5090M5 Max36 GB~51Simulate output on Mac Studio M5 Max (36 GB)M532 GB~17Simulate output on MacBook Air 13" M5 (32 GB)Orin~21Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.8 27B SLQ EAR99
Qwen 3.8 family · 12 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| SenseNova Loop 9B Previewsensenova | — | 8.2 GB | ~103 | same size |
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| ZDTaichu5.0 9BTaichuAI | — | 9.2 GB | ~101 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
Specifications
- Publisher
- kaitchup
- Family
- Qwen 3.8
- Parameters
- 9.2B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-10-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3.8 27B SLQ EAR99 need?
Qwen3.8 27B SLQ EAR99 requires 6.3 GB of VRAM at Q4_K_M, or 19.2 GB at BF16. Full 262K context adds up to 13.9 GB (20.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.2B × 4.8 bits ÷ 8 = 5.5 GB
KV Cache + Overhead ≈ 0.8 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 ≈ 14.7 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Qwen3.8 27B SLQ EAR99?
For Qwen3.8 27B SLQ EAR99, Q4_K_M (6.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.8 27B SLQ EAR99 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3.8 27B SLQ EAR99. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.8 27B SLQ EAR99 locally?
Yes — Qwen3.8 27B SLQ EAR99 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.8 27B SLQ EAR99?
At Q4_K_M, Qwen3.8 27B SLQ EAR99 can reach ~764 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~104 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 6.28 × 0.65 = ~828 tok/s
Estimated speed at Q4_K_M (6.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3.8 27B SLQ EAR99?
At Q4_K_M, the download is about 5.53 GB. The full-precision BF16 version is 18.43 GB. The smallest option (Q2_K) is 3.92 GB.
Which GPUs can run Qwen3.8 27B SLQ EAR99?
52 consumer GPUs can run Qwen3.8 27B SLQ EAR99 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen3.8 27B SLQ EAR99?
151 devices with unified memory can run Qwen3.8 27B SLQ EAR99 at Q4_K_M (6.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.