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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 50.8 GB at its Q4_K_M build vs 77.3 GB for Llama 4 Scout 109B's Q4_K_M, fitting on 44 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)

FP32
Llama 4 Scout 109B
491.3 GB
DeepSeek R1 Distill Llama 70B
316.6 GB
BF16
Llama 4 Scout 109B
247.2 GB
DeepSeek R1 Distill Llama 70B
159.8 GB
FP16
Llama 4 Scout 109B
247.2 GB
DeepSeek R1 Distill Llama 70B
159.8 GB
Q8_0
Llama 4 Scout 109B
132.8 GB
DeepSeek R1 Distill Llama 70B
86.3 GB
Q6_K
Llama 4 Scout 109B
103.2 GB
DeepSeek R1 Distill Llama 70B
67.4 GB
Q5_K_M
Llama 4 Scout 109B
89.9 GB
DeepSeek R1 Distill Llama 70B
58.8 GB
Q4_K_M
Llama 4 Scout 109B
77.3 GB
DeepSeek R1 Distill Llama 70B
50.8 GB
Q3_K_M
Llama 4 Scout 109B
61.7 GB
DeepSeek R1 Distill Llama 70B
40.7 GB
Q2_K
Llama 4 Scout 109B
49.5 GB
DeepSeek R1 Distill Llama 70B
32.9 GB
NVFP4
Llama 4 Scout 109B
64.0 GB
DeepSeek R1 Distill Llama 70B
42.2 GB
QuantLlama 4 Scout 109BDeepSeek R1 Distill Llama 70BDiff
FP32491.3 GB316.6 GB+55%
BF16247.2 GB159.8 GB+55%
FP16247.2 GB159.8 GB+55%
Q8_0132.8 GB86.3 GB+54%
Q6_K103.2 GB67.4 GB+53%
Q5_K_M89.9 GB58.8 GB+53%
Q4_K_M77.3 GB50.8 GB+52%
Q3_K_M61.7 GB40.7 GB+52%
Q2_K49.5 GB32.9 GB+51%
NVFP464.0 GB42.2 GB+52%

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)33 / 11944 / 119

Benchmark scores

BenchmarkLlama 4 Scout 109BDeepSeek R1 Distill Llama 70B
MMLU-Pro74.370.0

Green = higher score (better). N/A = not yet available. ~ = inherited from a base model, not independently reported for that release itself.

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(11)

GPUs that run both natively(33)

Which should you use?

Choose Llama 4 Scout 109B if:
  • • You want maximum capability and have a 78 GB+ GPU
  • • You want fast inference: MoE only activates 17B params per token
  • • Long context matters: it supports 9766k tokens vs 125k
  • • Benchmark quality matters: scores 74.3 vs 70.0 on MMLU-Pro
  • • You need vision/image understanding
  • • It's the newer release (2025-04-05 vs 2025-01-20); check the benchmark table above for what actually improved
Choose DeepSeek R1 Distill Llama 70B if:
  • • You have limited VRAM: it's a smaller model needing 50.8 GB vs 77.3 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 50.8 GB at its Q4_K_M build vs 77.3 GB for Llama 4 Scout 109B's Q4_K_M. DeepSeek R1 Distill Llama 70B runs on more GPUs natively (44 vs 33). On MMLU-Pro, Llama 4 Scout 109B scores higher (74.3 vs 70.0).
How much VRAM does Llama 4 Scout 109B need vs DeepSeek R1 Distill Llama 70B?
At 8k context, Llama 4 Scout 109B needs approximately 77.3 GB of VRAM at its Q4_K_M build, while DeepSeek R1 Distill Llama 70B needs 50.8 GB at its Q4_K_M build. At the largest build each ships, Llama 4 Scout 109B requires 247.2 GB (FP16) vs 159.8 GB (FP16) for DeepSeek R1 Distill Llama 70B.
Can you run Llama 4 Scout 109B on the same GPUs as DeepSeek R1 Distill Llama 70B?
Yes, 33 GPUs can run both natively in VRAM, including NVIDIA B300 288GB, NVIDIA B200 180GB, NVIDIA H200 141GB. However, no GPU can run Llama 4 Scout 109B without also fitting DeepSeek R1 Distill Llama 70B, and 11 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: Llama 4 Scout 109B needs 77.3 GB at Q4_K_M and DeepSeek R1 Distill Llama 70B needs 50.8 GB at Q4_K_M. Both require a multi-GPU server with 78 GB+ of combined VRAM.
Full Llama 4 Scout 109B page →Full DeepSeek R1 Distill Llama 70B page →Check your hardware →