What is the difference between Llama 3.3 70B and Llama 3.1 70B?

Llama 3.3 70B (Dec 2024) is a refined version that matches Llama 3.1 405B on many benchmarks despite having the same parameter count. It uses the same VRAM (~40 GB at Q4) but delivers significantly better quality. If you are choosing between them, Llama 3.3 is strictly better.

Can I run Llama 4 Scout on a single RTX 4090?

At standard Q4_K_M (~55 GB), no. With Unsloth's 1.78-bit dynamic quantization (~24 GB), yes — at about 20 tok/s with some quality trade-off. For full-quality Scout, you need dual GPUs or Apple Silicon with 64 GB+ unified memory.

Why do MoE models need VRAM for all parameters?

All expert weights must be loaded in memory because any expert could be activated for any token. The routing decision happens at inference time. You cannot predict which experts will be needed, so all must be available. This is why MoE helps speed (fewer active computations) but not memory (all weights loaded).

Is Llama 4 Behemoth available?

No. Meta announced it as a teacher model for training Scout and Maverick via codistillation. It has never been released as public weights and Meta has not committed to releasing it. Do not plan hardware purchases around Behemoth.

Which Llama model should I run on a 24 GB GPU?

Llama 3.1 8B at Q8_0 (~9 GB) for near-lossless quality. Llama 4 Scout at 1.78-bit (~24 GB) for maximum capability with some quality trade-off. You cannot run Llama 3.3 70B at Q4 on 24 GB — it needs ~40 GB.

Llama Family Guide: Llama 3 to Llama 4 — VRAM & Hardware

CanItRunJuly 1, 202610 min readVRAM Guides

The Llama Family: Four Generations of Open Weights

Meta's Llama family spans four generations and ranges from a 1B portable model to a 400B MoE powerhouse. Llama 3.1 (July 2024) established the baseline with 8B, 70B, and 405B dense models. Llama 3.2 added vision capabilities with 1B and 3B sizes. Llama 3.3 (December 2024) refined the 70B to match much larger models. Llama 4 (April 2025) introduced Mixture of Experts with Scout (109B/17B active) and Maverick (400B/17B active). Behemoth (2T/288B) was announced as a teacher model but remains unreleased as of July 2026.

Llama 3.1: The Foundation

Llama 3.1 8B (Q4_K_M ~5 GB, Q8_0 ~9 GB) is the most widely deployed open-weight model — fitting on virtually any modern GPU. Llama 3.1 70B (Q4_K_M ~40 GB) requires dual GPUs or Mac. Llama 3.1 405B (Q4 ~230 GB) is datacenter-only — the largest dense open model ever released, requiring 4-6 high-end GPUs. The 8B is excellent for general tasks on budget hardware. The 70B is the workhorse for serious local deployment. The 405B is a landmark but impractical for consumer hardware.

Llama 3.3 70B: The Efficiency Marvel

Released December 2024, Llama 3.3 70B achieves what was remarkable: it matches or exceeds Llama 3.1 405B on many benchmarks at less than one-fifth the parameter count. Q4_K_M requires ~38-40 GB — dual RTX 3090s (48 GB, ~$1500 used), RTX 5090 at Q3_K_M (~31 GB) single-card, or Apple M4 Max 128GB. On dual RTX 5090s it achieves 27 tok/s. For most users, Llama 3.3 70B represents the best balance of quality and hardware requirements in the Llama family.

Llama 4 Scout (109B MoE): The Practical Flagship

Llama 4 Scout uses a Mixture of Experts architecture: 109B total parameters across 16 experts, with only 17B active per token. This is THE critical MoE nuance: total parameters determine VRAM (all 109B expert weights must be loaded), while active parameters determine inference speed. Scout at Q4_K_M requires ~55 GB — dual RTX 4090s, Mac M4 Max 128GB, or a workstation GPU. Unsloth's 1.78-bit dynamic quantization fits Scout on a single 24 GB GPU at ~20 tok/s, with some quality trade-off. 10M token context window is the headline feature — usable for massive document analysis on appropriate hardware.

Llama 4 Maverick (400B MoE): Not for Consumers

Maverick scales the MoE architecture to 400B total parameters (128 experts, 17B active). Q4_K_M requires ~200 GB — this is firmly datacenter territory. On 8x H100, Maverick approaches GPT-4o-level performance. A single RTX 4090 with K-Transformers, CPU offloading, and 512 GB system RAM achieved 45 tok/s as a proof of concept — but this is experimental, not practical. Maverick demonstrates where open-weight models are heading, but for now, Scout is the practical Llama 4 for local inference.

MoE VRAM: The Most Common Misunderstanding

The #1 point of confusion with MoE models: total parameters determine VRAM requirements, not active parameters. All expert weights must be loaded into memory even though only a subset fires per token. Llama 4 Scout at 109B total needs ~55 GB at Q4, not the ~9 GB that 17B active parameters would suggest. This catches many beginners off guard. The benefit of MoE is speed, not memory: with only 17B active parameters computing per token, Scout generates tokens at roughly 17B-model speeds while delivering quality closer to a 109B dense model. For memory-constrained setups, dense models are actually more predictable.

GPU Recommendations by Llama Model

Llama 3.1 8B: any 8 GB+ GPU. Llama 3.2 3B: any GPU, runs on phones. Llama 3.3 70B: 2x RTX 3090 (48 GB), RTX 5090 at Q3, or M4 Max 128GB. Llama 3.1 405B: 5x A100 or cloud only. Llama 4 Scout: 2x RTX 4090 (48 GB), Mac M4 Max 128GB, or single 24 GB at 1.78-bit. Llama 4 Maverick: 4x H100 or cloud only. The best all-around Llama for consumer hardware in mid-2026 is Llama 3.3 70B on dual 3090s or Llama 4 Scout on M4 Max 128GB.

Frequently asked questions

What is the difference between Llama 3.3 70B and Llama 3.1 70B?: Llama 3.3 70B (Dec 2024) is a refined version that matches Llama 3.1 405B on many benchmarks despite having the same parameter count. It uses the same VRAM (~40 GB at Q4) but delivers significantly better quality. If you are choosing between them, Llama 3.3 is strictly better.
Can I run Llama 4 Scout on a single RTX 4090?: At standard Q4_K_M (~55 GB), no. With Unsloth's 1.78-bit dynamic quantization (~24 GB), yes — at about 20 tok/s with some quality trade-off. For full-quality Scout, you need dual GPUs or Apple Silicon with 64 GB+ unified memory.
Why do MoE models need VRAM for all parameters?: All expert weights must be loaded in memory because any expert could be activated for any token. The routing decision happens at inference time. You cannot predict which experts will be needed, so all must be available. This is why MoE helps speed (fewer active computations) but not memory (all weights loaded).
Is Llama 4 Behemoth available?: No. Meta announced it as a teacher model for training Scout and Maverick via codistillation. It has never been released as public weights and Meta has not committed to releasing it. Do not plan hardware purchases around Behemoth.
Which Llama model should I run on a 24 GB GPU?: Llama 3.1 8B at Q8_0 (~9 GB) for near-lossless quality. Llama 4 Scout at 1.78-bit (~24 GB) for maximum capability with some quality trade-off. You cannot run Llama 3.3 70B at Q4 on 24 GB — it needs ~40 GB.