Llama 4 Scout 17B-16E (109B) on Arc Pro B60 24GB (24GB) — VRAM, KV Cache & Throughput
Llama 4 Scout 17B-16E (109B) (109B total, 17.2B active, Sparse MoE + GQA) uses 61.95 GiB of 4-bit weights (Q4_K_M), requiring 64.01 GiB total at 8K context and 68.51 GiB at 32K on Arc Pro B60 24GB (~6.7 tok/s decode).
Exceeds Available GPU Memory
Config-derived formula
HF config.json ↗
Required GPU Memory
64.01 GiB
68.73 GB (decimal)
Usable Hardware Memory
22.8 GiB
24 GB (physical)
Safe Headroom
-41.21 GiB
267% utilization
Est. Decode Throughput
~6.7 tok/s
17.2B active params
Est. Prefill Throughput
~1512 tok/s
4K Prompt TTFT: ~2710 ms
Est. Cost / 1M Tokens
$18.33
Sparse MoE + GQA · Llama 4
Recommended Hardware for Current Configuration
Best Single GPU
RTX PRO 6000 Blackwell (96GB)
~$8,500
Best Value / Used GPU
2x A100 80GB SXM
160GB · ~$24,000
Apple Unified Memory
Ryzen AI Max+ 395 128GB
~$1,999
Cloud From
Vast.ai · $0.44 / hr
1x A100 80GB SXM
Production Launch Commands
llama.cpp (GGUF)
llama-server -hf unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF:Q4_K_M \ --ctx-size 8192 -ngl 999 --flash-attn
vLLM
vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct \ --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~2710ms · ~6.7 tok/s)
[Llama 4 Scout 17B-16E (109B) · 64.01 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...
Cloud GPU Comparison & Rent-vs-Buy TCO
Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.
Purchase break-even
~64.4 months
Est. local power: $12.77 / mo
| Provider | Recommended Instance | Total VRAM | Hourly Rate | Monthly at Selected Hours | Billing Mode | Deploy |
|---|---|---|---|---|---|---|
| RunPod | 1x A100 80GB SXM (Community Cloud · PCIe) | 80 GB | $1.19 / hr | $144.70 / mo | Per-second | Deploy GPU ↗ |
| Vast.ai | 1x A100 80GB SXM (Verified Host · SXM4) | 80 GB | $0.44 / hr | $53.75 / mo | Spot / On-demand | Deploy GPU ↗ |
| Vultr ($300 Free Credit*) | 1x A100 80GB SXM (Dedicated Instance · $300 Credit*) | 80 GB | $2.40 / hr | $291.48 / mo | Hourly | Deploy GPU ↗ |
| Vultr Cloud GPU | 1x A100 80GB SXM (Dedicated Instance) | 80 GB | $2.40 / hr | $291.48 / mo | Hourly | Deploy GPU ↗ |
| DigitalOcean | 1x Instinct MI300X 192GB (Dedicated Instance) | 192 GB | $2.59 / hr | $314.94 / mo | Per-second | Deploy GPU ↗ |
Rates reflect public listings on the verification date and fluctuate with regional inventory.
Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — Llama 4 Scout 17B-16E (109B)
QLoRA (4b)
Exceeds Available GPU Memory
65.47 GiB
Model Weights: 61.95 GiB · Gradients + Optimizer: 2.41 GiB · Act: 1.11 GiB
Recommended GPU: RTX PRO 6000 Blackwell (96GB)
LoRA (16b)
Exceeds Available GPU Memory
208.29 GiB
Model Weights: 203.03 GiB · Gradients + Optimizer: 3.78 GiB · Act: 1.48 GiB
Recommended GPU: Instinct MI325X 256GB (256GB)
Full BF16
Exceeds Available GPU Memory
835.02 GiB
Model Weights: 203.03 GiB · Gradients + Optimizer: 629.39 GiB · Act: 2.6 GiB
Recommended GPU: 8x H200 141GB SXM (1128GB)
Engineering Sizing Notes & Optimization Playbook
Quick Hardware Verdict
A single Arc Pro B60 24GB (24GB) cannot hold 4-bit Llama 4 Scout 17B-16E (109B) (64.01 GiB at 8K) entirely in VRAM. Use multi-GPU tensor parallelism, offload layers to system RAM, or deploy on a larger cloud GPU.
KV Cache & Runtime Tuning
Llama 4 Scout 17B-16E (109B) uses Sparse MoE + GQA (109B total, 17.2B active per token). Moving from 8K to 32K context expands FP16 KV cache from 1.5 GiB to 6 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 3 GiB at 32K (reducing total VRAM to 65.51 GiB).
VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)
Quantization Precision & Memory Matrix — Llama 4 Scout 17B-16E (109B) @ 8K
| Quantization Format | Weights (GiB) | KV Cache | Total VRAM (GiB) | Decode Speed | Compatibility (Arc Pro B60 24GB) | Recommended GPU | Data Source |
|---|---|---|---|---|---|---|---|
| Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16) | 203.03 GiB | 1.5 GiB | 205.09 GiB (220.21 GB) | ~1.7 tok/s | Exceeds Available GPU Memory | Instinct MI325X 256GB (256GB) | Official checkpoint size |
| FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) | 107.86 GiB | 1.5 GiB | 109.92 GiB (118.03 GB) | ~3.4 tok/s | Exceeds Available GPU Memory | Instinct MI300X 192GB (192GB) | Config-derived formula |
| GGUF Q6_K (6-bit — High Precision) (Q6_K) | 83.24 GiB | 1.5 GiB | 85.3 GiB (91.59 GB) | ~4.7 tok/s | Exceeds Available GPU Memory | RTX PRO 6000 Blackwell (96GB) | Config-derived formula |
| GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) | 72.41 GiB | 1.5 GiB | 74.47 GiB (79.96 GB) | ~5.5 tok/s | Exceeds Available GPU Memory | RTX PRO 6000 Blackwell (96GB) | Config-derived formula |
| Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M) | 61.95 GiB | 1.5 GiB | 64.01 GiB (68.73 GB) | ~6.7 tok/s | Exceeds Available GPU Memory | RTX PRO 6000 Blackwell (96GB) | Config-derived formula |
| GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) | 54.23 GiB | 1.5 GiB | 56.29 GiB (60.44 GB) | ~8 tok/s | Exceeds Available GPU Memory | RTX PRO 6000 Blackwell (96GB) | Config-derived formula |
| GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) | 49.88 GiB | 1.5 GiB | 51.94 GiB (55.77 GB) | ~9 tok/s | Exceeds Available GPU Memory | RTX PRO 6000 Blackwell (96GB) | Config-derived formula |
| GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) | 35.32 GiB | 1.5 GiB | 37.38 GiB (40.14 GB) | ~15.4 tok/s | Exceeds Available GPU Memory | A40 48GB (48GB) | Config-derived formula |
Technical Specifications & FAQ
Does Llama 4 Scout 17B-16E (109B) fit on Arc Pro B60 24GB (24GB)?
In 4-bit (Q4_K_M) at 8,192 context, Llama 4 Scout 17B-16E (109B) requires 64.01 GiB total GPU memory (61.95 GiB weights + 1.5 GiB KV cache + 0.56 GiB runtime buffers) against 22.8 GiB usable on Arc Pro B60 24GB, delivering ~6.7 tok/s decode.
How much VRAM does Llama 4 Scout 17B-16E (109B) need at 32K context?
At 32,768 context tokens, Llama 4 Scout 17B-16E (109B) (Sparse MoE + GQA) allocates 6 GiB for KV cache and state, bringing total 4-bit VRAM to 68.51 GiB (73.56 GB decimal).
Evaluate This Model on Other GPUs
RTX 3060 12GB (12GB)RTX 4060 Ti 16GB (16GB)RTX 4070 Ti Super (16GB)RTX 4080 Super (16GB)RTX 5070 12GB (12GB)RTX 5070 Ti (16GB)RTX 5080 (16GB)RTX 3090 (24GB)RTX 4090 (24GB)RTX 4090 D (24GB)RTX 5090 (32GB)RTX 5090 D (32GB)RTX A6000 (48GB)RTX 6000 Ada (48GB)RTX PRO 6000 Blackwell (96GB)DGX Spark (GB10 128GB) (128GB)L4 24GB (24GB)A40 48GB (48GB)L20 48GB (48GB)L40S 48GB (48GB)A800 80GB SXM (80GB)A100 80GB SXM (80GB)H800 80GB SXM (80GB)H100 80GB SXM (80GB)H20 96GB HBM3 (96GB)H20 141GB HBM3e (141GB)H200 141GB SXM (141GB)B200 180GB (180GB)B300 288GB Blackwell Ultra (288GB)Ascend 910B 64GB (昇腾) (64GB)Ascend 910C 128GB (昇腾) (128GB)Atlas 300I Duo 96GB (310P) (96GB)MLU590 80GB (寒武纪思元) (80GB)DCU K100-AI 64GB (海光深算) (64GB)MetaX C500 64GB (沐曦曦云) (64GB)Kunlunxin P800 64GB (昆仑芯) (64GB)MTT S4000 48GB (摩尔线程) (48GB)Radeon RX 7900 XTX 24GB (24GB)Radeon AI PRO R9700 32GB (32GB)Ryzen AI Max+ 395 128GB (128GB)Instinct MI300X 192GB (192GB)Instinct MI325X 256GB (256GB)Instinct MI355X 288GB (288GB)Gaudi 3 128GB HBM2e (128GB)Mac mini M4 Pro 64GB (64GB)MacBook Pro M4 Max 128GB (128GB)Mac Studio M4 Ultra 256GB (256GB)Mac Studio M3 Ultra 512GB (512GB)
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