llama

109B parameters

Commercial OK

Reviewed October 2026

Llama 4 Scout

Meta's 2026 flagship MoE model. 109B total parameters with only 17B active per forward pass and a record 10-million-token context window — unmatched in production at any tier. Built for long-document workflows, RAG over entire codebases, and continuous-context agents.

License: Llama 4 Community License·Released Apr 5, 2026·Context: 10,000,000 tokens

BLK · VERDICT

Our verdict

OP · Eruo Fredoline|UPDATED OCT 7, 2026

Not tested by usEditorial assessment from specifications and published reports.

8.4/10

Positioning

Llama 4 Scout is Meta's "small flagship" of the new generation — natively multimodal, MoE architecture (109B total, 17B active), and the first Llama with a serious long-context story. It's the Llama 3.3 70B replacement for users with ~64 GB VRAM or unified memory.

Strengths

Native vision-language — single model handles image+text without a separate adapter, unlike Llama 3.2 11B Vision's bolted-on approach.
No configuration-matched speed measurement is linked for this option.
Architectural long context that genuinely works further than 32K — recall stays competitive into 100K territory in practice.

Limitations

109B total params mean Q4 is ~62 GB — needs dual high-VRAM cards, an A6000-class workstation, or Apple Silicon with 96 GB+ unified memory.
License added new clauses vs Llama 3 — review the AUP if you ship at scale.
Vision quality is solid but not best-in-class — Pixtral and Qwen 2.5 VL still edge it on dense OCR and chart understanding.

Performance evidence

The earlier timing ranges were not tied to a published run. Use the benchmark records for measured results and compare their full configurations.

Should you run this locally?

Yes, for workstation rigs (dual 4090, A6000, RTX 6000 Ada) and high-RAM Mac Studios. Excellent native multimodal model. No, for single-card consumer setups — at Q4 you're CPU-offloaded; at lower quants, quality erodes faster than usual on MoE.

How it compares

vs Llama 3.3 70B → Scout is multimodal and has better architectural long context; Llama 3.3 70B is faster on a single 24 GB card. Pick Scout if you have the memory and want vision; otherwise stick with 3.3 70B.
vs Llama 4 Maverick → Maverick is the bigger sibling (400B/17B active). Same active compute but Maverick has a much larger expert pool — better quality if you can afford the disk + memory.
vs Qwen 2.5 VL 72B → Qwen 2.5 VL is stronger on dense visual reasoning; Scout is more usable as a general assistant. Different jobs.

Run this yourself

ollama pull llama4:scout
ollama run llama4:scout

Example settings (not a measured run): Q4_K_M GGUF, 16384 ctx, --n-gpu-layers 30 of 49, RTX 4090 + 64 GB DDR5

›Why this rating

8.4/10 — the smallest Llama 4 is the model most local users will actually run, with native multimodality and a 10M-context architecture. Loses points only because real-world recall over the full advertised context is still imperfect.

Overview

Featured in this stack

The L3 execution stacks that pick this model as a recommended component, with the one-line note explaining the role it plays in each.

Stack · L3·Workstation tier·Role: Primary multimodal model (text + vision)
Build a local vision-model stack (May 2026)
Llama 4 Scout is the multimodal flagship in the open Llama 4 family. Strong image understanding combined with the same reasoning quality the text-only Llama 4 line delivers. The pick when you need image-grounded analysis at frontier-tier quality.

Execution notes

L1.25 enriched

Operator notes

Llama 4 Scout is the production multimodal flagship in the Llama 4 line for the workstation-cluster tier (datacenter-class deployment, not consumer-tier). Apache-equivalent Llama Community License. Strong on image-text reasoning at the workstation-cluster scale; the right pick when you need multimodal capability above the consumer Pixtral / Qwen-VL tier but don't need Maverick's frontier-cluster cost.

Deployment notes

The /stacks/local-vision-model recipe pairs this model with vLLM on RTX 4090 (tight, AWQ-INT4 fits with vision-token headroom carefully managed). Production-tier deployment is 2x A100 80GB or 1x H100 with comfortable headroom for multi-image queries.

For consumer-tier multimodal, drop to Pixtral 12B or Qwen 2.5-VL 7B — same multimodal capability shape, smaller hardware envelope.

For frontier-tier multimodal, Llama 4 Maverick is the parent — better image understanding at the multi-node cluster cost.

Runtime compatibility

vLLM ✓ excellent. Vision-language support landed in v0.7+; multi-image inputs handled cleanly.
SGLang ✓ good. Vision support younger than vLLM's — verify on your specific multi-image patterns.
Ollama ✓ partial. Vision-model support landed but vLLM has the production lead for multimodal workloads.
TensorRT-LLM ✗ partial. Multimodal support exists but the per-model recompile friction is severe for vision.
MLX-LM ✓ partial. Apple Silicon multimodal path is younger; Pixtral and Qwen 2.5-VL have stronger MLX integration.

Vision token economics

Vision-language models tokenize images as long sequences of vision tokens — Llama 4 Scout uses approximately 512 vision tokens per 1024×1024 image at default resolution. Multi-image queries (5 images at 512 tokens each) consume 2560 tokens of context just on images, before the user prompt or model response. Plan KV-cache budget accordingly.

Best use cases

Production multimodal serving at the workstation-cluster tier — image-Q&A, document understanding, visual reasoning.
Multi-image queries — the model handles 5-10 images per query cleanly.
Document layout + OCR + reasoning combined — strong on mixed-content workloads.
Llama-ecosystem migration path — drop-in for teams already on Llama 3 with multimodal needs.

When to use a different model

Consumer-tier multimodal (16-24GB VRAM): use Pixtral 12B or Qwen 2.5-VL 7B.
Frontier-tier multimodal: use Llama 4 Maverick — same family, larger.
Apple Silicon multimodal: Pixtral 12B has stronger MLX integration today.
OCR-first workloads: dedicated OCR models (Florence-2, MiniCPM-V) often beat general VLMs at text extraction.
Apache 2.0 license required: Pixtral 12B or Qwen 2.5-VL 72B — clean Apache 2.0.

Failure modes specific to this model

OOM on multi-image queries. A 5-image query blows past KV-cache budgets sized for text-only workloads. Lower `--max-num-seqs` to 4 or 2.
Image-format mismatch. Some vision models require RGB; obscure formats (TIFF, RAW) fail. Pre-convert client-side.
Resolution silently downsampled. Default vision encoder downsamples; high-detail tasks (small text OCR) need explicit higher-resolution model variants.
Mixed-modality tool-call format. Some agent harnesses can't handle text+image content blocks correctly.

Going deeper

/stacks/local-vision-model — the canonical deployment recipe
Llama 4 Maverick — the parent / larger family member
Pixtral 12B, Qwen 2.5-VL 7B — consumer-tier multimodal alternatives
vLLM operational review — multimodal serving runtime

Reviewed May 6, 2026 by Eruo Fredoline

Family & lineage

How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.

Parent / base model

Llama 4 Maverick400B

Frontier

Family siblings (llama-4)

Llama 4 Scout109B

You are here

Llama 4 Maverick400B

Frontier

Strengths

10M token context (industry-leading)
Efficient MoE — runs at 17B-active speed
Strong tool/function calling

Weaknesses

Total weights still need 65GB+ VRAM at Q4
Long-context attention is RAM-hungry
Newer than Llama 3.x — less ecosystem battle-testing

Quantization variants

Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.

Quantization	File size	VRAM required
Q4_K_M	65.0 GB	80 GB
Q5_K_M	78.0 GB	95 GB
FP16	218.0 GB	240 GB

Rent instead

Too big for a 32 GB card? Rent a GPU by the hour

At Q4 Llama 4 Scout needs about 80 GB of memory by our estimate, more than a 32 GB graphics card holds. Renting a GPU with enough memory (or several GPUs) by the hour is the practical way to try it before you plan hardware for it.

Referral links: RunLocalAI may earn a commission or account credits if you sign up and rent, at no extra cost to you. Hourly rates and which GPUs are listed change all the time, so check the provider's page before you start. How we make money.