This article was originally published on runaihome.com
TL;DR: Llama 4 Maverick has 17B active parameters but 402B total parameters spread across 128 experts — the Q4_K_M GGUF file is 243GB and needs four H100 80GB cards to load. The only consumer path is Mac Studio M4 Max running IQ1_78 (89–122GB), which degrades quality enough that Scout Q4_K_M locally beats it. For real Maverick quality, RunPod at ~$6.58–$13.16/hr is the honest answer.
| Mac Studio M4 Max 128GB | RunPod 2× H100 SXM | RunPod 4× H100 SXM | |
|---|---|---|---|
| Best for | Ultra-compressed Maverick IQ1_78 | Q3_K_M, strong quality | Q4_K_M, full quality |
| VRAM / RAM | 128GB unified | 160GB | 320GB |
| Quantization | IQ1_78 (~122GB) | Q3_K_M (~150GB) | Q4_K_M (~243GB) |
| Speed (est.) | ~15–30 tok/s | ~40–60 tok/s | ~60–90 tok/s |
| Cost | $1,999+ one-time | ~$6.58/hr | ~$13.16/hr |
| The catch | Severe quality degradation | Hourly costs add up fast | $316/day at 24/7 |
Honest take: Run Scout locally on an RTX 4090 or RTX 5060 Ti for daily use; rent RunPod 2×H100 by the hour when you need Maverick's reasoning ceiling for specific high-stakes tasks.
The 17B Active Parameter Trap — Again
If you've already read the Llama 4 Scout hardware guide, you know the trap. Scout's headline — "17B active parameters" — made everyone assume it would run on a 16GB card. It doesn't, because Scout has 109B total parameters that all have to be loaded into memory before inference begins.
Maverick pulls the exact same trick, but at 8× the scale.
Both Scout and Maverick activate roughly 17B parameters per token during inference. The difference is in how many expert networks they carry:
- Scout: 16 experts × ~6.4B parameters each = 109B total
- Maverick: 128 experts × ~3.1B parameters each = 402B total
The router chooses which 1–2 experts handle each token. The other 126–127 experts sit idle during that token. But they still have to be in memory — you don't know which expert the router will pick until runtime, so all 402B parameters need to be loaded before the first token generates.
That "17B active" number describes compute throughput at inference time. It does not describe VRAM requirements. This distinction matters more for Maverick than for any other consumer-facing model today.
The VRAM Math: What Each Quantization Actually Requires
At 402B parameters, every quantization level lands in a different tier of hardware:
| Quantization | Approx. File Size | Min VRAM Needed | Realistic Hardware |
|---|---|---|---|
| FP16 | ~801 GB | ~10× H100 80GB | Datacenter clusters only |
| FP8 / INT8 | ~422 GB | 6× H100 80GB | Enterprise multi-node |
| Q4_K_M | ~243 GB | 4× H100 80GB | RunPod 4×H100 at $13.16/hr |
| Q3_K_M | ~150 GB | 2× H100 80GB | RunPod 2×H100 at $6.58/hr |
| Q2_K | ~100 GB | 2× A100 80GB or L40S | Tight; CPU offload needed |
| IQ1_78 (1.78-bit) | ~89–122 GB | Mac Studio M4 Max 128GB | Only "consumer" option |
The math behind Q4_K_M: 402B parameters × 4 bits / 8 bits per byte = ~201GB of raw weights. The actual GGUF file distributed by Unsloth lands at roughly 243GB (split across five ~49GB files) once you add metadata, normalization layers, and GGUF framing overhead.
You can pull it in Ollama with:
ollama pull llama4:17b-maverick-128e-instruct-q4_K_M
That's a 243GB download. On a 1 Gbps home connection, that's roughly 33 minutes. On typical US residential (400 Mbps), closer to 80 minutes. And once it's pulled, it won't run unless you have 243GB of VRAM — which no consumer GPU configuration reaches.
The RTX 5090's 32GB of GDDR7 is the largest single consumer GPU available today. Even eight of them in a hypothetical workstation (256GB total) would barely fit Q4_K_M. There is no consumer path to Maverick at Q4 quality.
The Only "Consumer" Path: Mac Studio M4 Max at IQ1_78
Apple Silicon's unified memory architecture is the closest thing to a consumer-grade Maverick option. The Mac Studio M4 Max tops out at 128GB of unified memory — the same pool is accessible to both CPU and GPU, and llama.cpp can load and run models that fit in that pool.
At IQ1_78 quantization (1.78 bits per parameter), Maverick compresses down to ~89–122GB. That fits in 128GB with enough headroom for macOS and a multi-thousand-token context.
The obvious question is: what does 1.78-bit quantization do to quality?
The honest answer is: a lot. IQ1_78 is at the extreme low end of the quality-preservation curve. Llama.cpp's importance-weighted quantization (IQ series) is better than naive low-bit formats, but 1.78 bits per parameter is roughly 4.5× more compressed than Q4_K_M. You will notice it on complex reasoning, multi-step math, and instruction-following tasks where the model needs to maintain state across a long chain of logic.
On speed: the M4 Max chip provides ~546 GB/s of unified memory bandwidth. Maverick's MoE architecture means only the active expert's weights (~3.8GB per token at IQ1_78) need to be read per forward pass. Estimated throughput at typical llama.cpp efficiency is ~15–30 tok/s — useful for long-context document processing where you're not waiting for real-time output.
In practice: IQ1_78 Maverick on a Mac Studio M4 Max will often underperform Q4_K_M Scout on a 24GB GPU on reasoning benchmarks. You're trading a faster, more capable model (Maverick) for a version degraded so far that the underlying advantage evaporates.
If you already own a Mac Studio M4 Max 128GB, it's worth testing IQ1_78 Maverick for curiosity's sake. But don't buy the hardware for this purpose.
The Cloud Math: RunPod for Real Maverick Quality
For researchers, consultants, or indie devs who need Maverick's actual capabilities — not an IQ1_78 approximation — RunPod is the practical path.
RunPod H100 SXM pricing (on-demand Secure Cloud, as of June 2026): $3.29/hr per GPU.
This gives you:
| Configuration | VRAM | Best Quantization | Est. Speed | Hourly Cost |
|---|---|---|---|---|
| 2× H100 SXM | 160 GB | Q3_K_M (~150GB) | ~30–40 tok/s | $6.58/hr |
| 4× H100 SXM | 320 GB | Q4_K_M (~243GB) | ~50–70 tok/s | $13.16/hr |
Q3_K_M on 2×H100 is the sweet spot. At ~150GB, it sits comfortably within 160GB combined VRAM (enough headroom for KV cache and context). Quality at Q3_K_M is substantially better than IQ1_78 — you're only one quantization step below Q4, not six.
For typical usage patterns — a few hours of inference work per week — the math works. Three hours on RunPod 2×H100 costs about $19.74. That's less than a monthly API subscription to most frontier models, and you get full control over context length, system prompts, and model weights.
Quick RunPod setup for Maverick Q3_K_M:
# On your RunPod pod (once connected via SSH)
pip install huggingface_hub
# Download Q3_K_M from Unsloth's GGUF repo
huggingface-cli download \
unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF \
--include "Q3_K_M/*" \
--local-dir ./maverick-q3
# Run with llama.cpp (adjust -ngl for full GPU offload)
./llama-server \
-m ./maverick-q3/Llama-4-Maverick-17B-128E-Instruct-Q3_K_M.gguf \
-ngl 999 \
--port 8080 \
-c 8192
RunPod provisions the pod with CUDA drivers and you can install llama.cpp in a few minutes. The model download from HuggingFace Hub at datacenter speeds takes 15–25 minutes depending on which RunPod region you pick.
For multi-GPU cloud inference on RunPod, start with a 2×H100 Community Cloud pod (cheaper, spot-like availability) and upgrade to Secure Cloud if you need guaranteed uptime for a longer project.
Scout vs Maverick: Is the 8–12 Point Gap Worth It?
Maverick isn't just a bigger Scout. The 128-expert architecture gives it meaningfully better reasoning, especially on tasks that require diverse knowledge retrieval and cross-domain synthesis.
Benchmark comparison at la
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