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Llama 4 Scout 109B vs DeepSeek R1 Distill Llama 70B

Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.

Quick verdict

DeepSeek R1 Distill Llama 70B is more hardware-efficient — it needs 42.2 GB at Q4_K_M vs 64.0 GB for Llama 4 Scout 109B, fitting on 38 GPUs natively. Llama 4 Scout 109B is a Mixture of Experts model — it has 109B total parameters but only 17B are active per token, making inference faster than its total size suggests.

VRAM at each quantization (8k context)

QuantLlama 4 Scout 109BDeepSeek R1 Distill Llama 70BDiff
FP16247.2 GB159.8 GB+55%
Q8125.1 GB81.4 GB+54%
Q6_K94.6 GB61.8 GB+53%
Q5_K_M79.3 GB52.0 GB+52%
Q4_K_M64.0 GB42.2 GB+52%
Q3_K_M51.8 GB34.4 GB+51%
Q2_K39.6 GB26.5 GB+49%

Diff is Llama 4 Scout 109B relative to DeepSeek R1 Distill Llama 70B. Green = lower VRAM (fits more GPUs).

Model specifications

SpecLlama 4 Scout 109BDeepSeek R1 Distill Llama 70B
OrgMetaDeepSeek
Parameters109B70B
ArchitectureMoE (17B active)Dense
Context9766k tokens125k tokens
Modalitiestext, visiontext
LicenseLlama 4 CommunityMIT
CommercialYesYes
Released2025-04-052025-01-20
GPUs (native)28 / 6738 / 67

Benchmark scores

BenchmarkLlama 4 Scout 109BDeepSeek R1 Distill Llama 70B
MMLU-Pro70.070.0

Green = higher score (better). — = not yet available.

GPUs that run only Llama 4 Scout 109B(0)

Every GPU that runs Llama 4 Scout 109B also runs DeepSeek R1 Distill Llama 70B.

GPUs that run only DeepSeek R1 Distill Llama 70B(10)

GPUs that run both natively(28)

Which should you use?

Choose Llama 4 Scout 109B if:
  • • You want maximum capability and have a 65 GB+ GPU
  • • You want fast inference — MoE only activates 17B params per token
  • • Long context matters — it supports 9766k tokens vs 125k
  • • You need vision/image understanding
Choose DeepSeek R1 Distill Llama 70B if:
  • • You have limited VRAM — it's a smaller model needing 42.2 GB vs 64.0 GB
  • • You need chain-of-thought reasoning

Frequently asked questions

Which is better, Llama 4 Scout 109B or DeepSeek R1 Distill Llama 70B?
Llama 4 Scout 109B has 109B parameters vs 70B for DeepSeek R1 Distill Llama 70B, so Llama 4 Scout 109B is the larger model. DeepSeek R1 Distill Llama 70B is more hardware-efficient, needing 42.2 GB at Q4_K_M vs 64.0 GB. DeepSeek R1 Distill Llama 70B runs on more GPUs natively (38 vs 28). On MMLU-Pro, DeepSeek R1 Distill Llama 70B scores higher (70.0 vs 70.0).
How much VRAM does Llama 4 Scout 109B need vs DeepSeek R1 Distill Llama 70B?
At Q4_K_M quantization with 8k context, Llama 4 Scout 109B needs approximately 64.0 GB of VRAM, while DeepSeek R1 Distill Llama 70B needs 42.2 GB. At FP16, Llama 4 Scout 109B requires 247.2 GB vs 159.8 GB for DeepSeek R1 Distill Llama 70B.
Can you run Llama 4 Scout 109B on the same GPUs as DeepSeek R1 Distill Llama 70B?
Yes, 28 GPUs can run both natively in VRAM, including NVIDIA H100 80GB, NVIDIA A100 80GB, NVIDIA L40S. However, no GPU can run Llama 4 Scout 109B without also fitting DeepSeek R1 Distill Llama 70B, and 10 GPUs can run DeepSeek R1 Distill Llama 70B but not Llama 4 Scout 109B.
What is the difference between Llama 4 Scout 109B and DeepSeek R1 Distill Llama 70B?
Llama 4 Scout 109B has 109B parameters (17B active, MoE) with a 9766k context window. DeepSeek R1 Distill Llama 70B has 70B parameters (dense) with a 125k context window. Licensing differs: Llama 4 Scout 109B is Llama 4 Community while DeepSeek R1 Distill Llama 70B is MIT.
Which model fits in 24 GB of VRAM, Llama 4 Scout 109B or DeepSeek R1 Distill Llama 70B?
Neither fits in 24 GB at Q4_K_M — Llama 4 Scout 109B needs 64.0 GB and DeepSeek R1 Distill Llama 70B needs 42.2 GB. Both require at least a 48 GB GPU.
Full Llama 4 Scout 109B page →Full DeepSeek R1 Distill Llama 70B page →Check your hardware →