Qwen3.8 Flash Next Tq4a Tq2e G64 — Hardware Requirements & GPU Compatibility
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Qwen3.8 Flash Next Tq4a Tq2e G64 is a 16.6B-parameter open language model from manjunathshiva in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 10.35 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~63 tok/s · 256K)Macs from 16 GB (Q4_K_M · ~14 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.8 Flash Next Tq4a Tq2e G64
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 manjunathshiva/Qwen3.8-Flash-Next-tq4a-tq2e-g64 --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 3090 (24 GB) (13 GB headroom) · Q4_K_M
- Generation speed
- ~59 tok/s
- generation speed
- Cost per 1M output tokens
- $0.93
- per 1M output tokens
How Much VRAM Does Qwen3.8 Flash Next Tq4a Tq2e G64 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 7.4 GB | 8.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~88 tok/s | |
| 8.5 GB | 9.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~77 tok/s | |
| 10.3 GB | 11.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~63 tok/s | |
| 12.2 GB | 13.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~54 tok/s | |
| 14.1 GB | 15.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~47 tok/s | |
| 17.0 GB | 18.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~39 tok/s | |
| 33.6 GB | 34.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 34.2 GB |
Selected Q4_K_M
4-bit medium quantization10.9GBat 16K context
- Weights
- 10.0 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.1 GB free · ~63 tok/s
≥50 tok/s needs ≥795 GB/s · RTX 5090 ~113, RTX 3090 Ti ~63
Which Hardware Can Run Qwen3.8 Flash Next Tq4a Tq2e G64?
Q4_K_M · 10.3 GBQwen3.8 Flash Next Tq4a Tq2e G64 (Q4_K_M) requires 10.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 262K context window can add up to 10.7 GB, bringing total usage to 21 GB. 38 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Qwen3.8 Flash Next Tq4a Tq2e G64, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 1.1 GB free · up to 32K ctx
10 GPUs
RTX 3080 Ti~57Simulate output on RTX 3080 TiRTX 5070~42Simulate output on RTX 5070RTX 4070~32Simulate output on RTX 407020 GB
fits · 9.1 GB free · up to 128K ctx
1 GPU
RX 7900 XT~46Simulate output on RX 7900 XT32–40 GB
fits · 21.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~113Simulate output on RTX 5090M5 Max36 GB~31Simulate output on Mac Studio M5 Max (36 GB)M532 GB~10Simulate output on MacBook Air 13" M5 (32 GB)Orin~13Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~9Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.8 Flash Next Tq4a Tq2e G64
Qwen 3.8 family · 12 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Kimi VL A3B InstructMoonshot AI | — | 10.7 GB | ~208 | same size |
| LLaDA2.1 MiniInclusion AI | — | 33.5 GB | —needs > 24 GB | same size |
| LLaDA2.0 MiniInclusion AI | — | 10.7 GB | ~406 | same size |
| Moonlight 16B A3BMoonshot AI | — | 10.1 GB | ~229 | same size |
| DeepSeek v2 LiteDeepSeek | — | 10.2 GB | ~243 | same size |
| Ling Mini 2.0Inclusion AI | — | 10.7 GB | ~406 | same size |
Specifications
- Publisher
- manjunathshiva
- Family
- Qwen 3.8
- Parameters
- 16.6B
- Architecture
- Qwen4ExpForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-14
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen3.8 Flash Next Tq4a Tq2e G64 need?
Qwen3.8 Flash Next Tq4a Tq2e G64 requires 10.3 GB of VRAM at Q4_K_M, or 33.6 GB at BF16. Full 262K context adds up to 10.7 GB (21 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 16.6B × 4.8 bits ÷ 8 = 10 GB
KV Cache + Overhead ≈ 0.3 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 ≈ 11 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen3.8 Flash Next Tq4a Tq2e G64?
Yes, at Q8_0 (17.0 GB) or lower. Higher quantizations like BF16 (33.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3.8 Flash Next Tq4a Tq2e G64?
For Qwen3.8 Flash Next Tq4a Tq2e G64, Q4_K_M (10.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (12.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.8 Flash Next Tq4a Tq2e G64 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3.8 Flash Next Tq4a Tq2e G64. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 10.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.8 Flash Next Tq4a Tq2e G64 locally?
Yes — Qwen3.8 Flash Next Tq4a Tq2e G64 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.8 Flash Next Tq4a Tq2e G64?
At Q4_K_M, Qwen3.8 Flash Next Tq4a Tq2e G64 can reach ~464 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~63 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 ÷ 10.35 × 0.65 = ~502 tok/s
Estimated speed at Q4_K_M (10.3 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 Flash Next Tq4a Tq2e G64?
At Q4_K_M, the download is about 9.96 GB. The full-precision BF16 version is 33.20 GB. The smallest option (Q2_K) is 7.06 GB.
Which GPUs can run Qwen3.8 Flash Next Tq4a Tq2e G64?
38 consumer GPUs can run Qwen3.8 Flash Next Tq4a Tq2e G64 at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen3.8 Flash Next Tq4a Tq2e G64?
150 devices with unified memory can run Qwen3.8 Flash Next Tq4a Tq2e G64 at Q4_K_M (10.3 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.