Llama 4 Scout 17B 16E Instruct Q8 0 — Hardware Requirements & GPU Compatibility
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Llama 4 Scout 17B 16E Instruct Q8 0 is a 17B-parameter open language model from Mogith in the Llama 4 family. It supports a context window of up to 10,485,760 tokens. At Q4_K_M it needs about 10.90 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 · 4K)Best on 24 GB (Q4_K_M · ~60 tok/s · 64K)Macs from 16 GB (Q4_K_M · ~13 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 4 Scout 17B 16E Instruct Q8 0
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Mogith/Llama-4-Scout-17B-16E-Instruct-Q8_0 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX A5000 (24 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~46 tok/s
- generation speed
- Cost per 1M output tokens
- $0.97
- per 1M output tokens
How Much VRAM Does Llama 4 Scout 17B 16E Instruct Q8 0 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 7.9 GB | 14 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~83 tok/s | |
| 9.0 GB | 15 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~73 tok/s | |
| 10.9 GB | 16.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~60 tok/s | |
| 12.8 GB | 18.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~51 tok/s | |
| 14.7 GB | 20.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~45 tok/s | |
| 17.7 GB | 23.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~37 tok/s | |
| 34.7 GB | 40.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 37.5 GB |
Selected Q4_K_M
4-bit medium quantization13.7GBat 16K context
- Weights
- 10.2 GB
- KV cache
- 3.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 10.3 GB free · ~60 tok/s
≥50 tok/s needs ≥838 GB/s · RTX 5090 ~107, RTX 3090 Ti ~60
Which Hardware Can Run Llama 4 Scout 17B 16E Instruct Q8 0?
Q4_K_M · 10.9 GBLlama 4 Scout 17B 16E Instruct Q8 0 (Q4_K_M) requires 10.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 15+ GB is recommended. Using the full 10486K context window can add up to 2061.2 GB, bringing total usage to 2072.1 GB. 38 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080. 150 devices with unified memory can run Llama 4 Scout 17B 16E Instruct Q8 0, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
16–18 GB
16–18 GB tier →fits · 2.3 GB free · up to 16K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~57Simulate output on RTX 5080RTX 5070 Ti~53Simulate output on RTX 5070 TiRTX 4080 SUPER~44Simulate output on RTX 4080 SUPERM516 GB~10Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~10Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~8Simulate output on iPad Pro M4 13" (16 GB)Orin NX~6Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 6.3 GB free · up to 32K ctx
1 GPU
RX 7900 XT~44Simulate output on RX 7900 XT32–40 GB
fits · 18.3 GB free · up to 64K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~107Simulate output on RTX 5090M5 Max36 GB~30Simulate output on Mac Studio M5 Max (36 GB)M532 GB~10Simulate output on MacBook Air 13" M5 (32 GB)Orin~12Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~8Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
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 |
| Ling Mini 2.0Inclusion AI | — | 10.7 GB | ~406 | same size |
| Deepseek Moe 16B ChatDeepSeek | — | 11.1 GB | ~231 | same size |
Specifications
- Publisher
- Mogith
- Family
- Llama 4
- Parameters
- 17B
- Architecture
- Llama4ForConditionalGeneration
- Context Length
- 10,485,760 tokens
- Vocabulary Size
- 202,048
- Release Date
- 2025-04-09
- License
- Other
Frequently Asked Questions
How much VRAM does Llama 4 Scout 17B 16E Instruct Q8 0 need?
Llama 4 Scout 17B 16E Instruct Q8 0 requires 10.9 GB of VRAM at Q4_K_M, or 34.7 GB at BF16. Full 10486K context adds up to 2061.2 GB (2072.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 17B × 4.8 bits ÷ 8 = 10.2 GB
KV Cache + Overhead ≈ 0.7 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 ≈ 2061.9 GB (at full 10486K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Llama 4 Scout 17B 16E Instruct Q8 0?
Yes, at Q8_0 (17.7 GB) or lower. Higher quantizations like BF16 (34.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Llama 4 Scout 17B 16E Instruct Q8 0?
For Llama 4 Scout 17B 16E Instruct Q8 0, Q4_K_M (10.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (12.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 4 Scout 17B 16E Instruct Q8 0 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Llama 4 Scout 17B 16E Instruct Q8 0. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 10.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 4 Scout 17B 16E Instruct Q8 0 locally?
Yes — Llama 4 Scout 17B 16E Instruct Q8 0 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 4 Scout 17B 16E Instruct Q8 0?
At Q4_K_M, Llama 4 Scout 17B 16E Instruct Q8 0 can reach ~440 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~60 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 ÷ 10.9 × 0.65 = ~477 tok/s
Estimated speed at Q4_K_M (10.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 4 Scout 17B 16E Instruct Q8 0?
At Q4_K_M, the download is about 10.20 GB. The full-precision BF16 version is 34.00 GB. The smallest option (Q2_K) is 7.22 GB.
Which GPUs can run Llama 4 Scout 17B 16E Instruct Q8 0?
38 consumer GPUs can run Llama 4 Scout 17B 16E Instruct Q8 0 at Q4_K_M. Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6700 XT. 8 GPUs have plenty of headroom for comfortable inference.
Which devices can run Llama 4 Scout 17B 16E Instruct Q8 0?
150 devices with unified memory can run Llama 4 Scout 17B 16E Instruct Q8 0 at Q4_K_M (10.9 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.