Does the Llama 4 Community license pose a problem for a startup?

No fewer than 700 million monthly active users. The "Built with Llama" attribution clause and acceptable use policies remain the only meaningful commitments. For a project targeting a purely permissive license, DeepSeek V4 Pro under MIT or Mistral Large 3 675B under Apache 2.0 are preferable.

Llama 4 Maverick 400B vs DeepSeek V4 Pro 1.6T

Q: What is the minimum VRAM needed to run DeepSeek V4 Pro 1.6T locally?

Expect about 960 GB of VRAM in Q4_K_M (estimated). This requires at least 12 H100 80GB GPUs with tensor parallelism, or a B200 cluster. Experimental Q2 quantization (llama.cpp i-quants) can go down to ~500 GB but severely degrades reasoning. No current Mac can fit the model, even in Q2.

Q: Does Llama 4 fit on a Mac Studio M3 Ultra 512 GB?

Yes, with Q4_K_M quantization (~240 GB), leaving comfortable headroom for the KV cache. Observed throughput is around 8–15 tokens/sec (estimated), depending on context length. To compare with other Mac deployments, see the LLM guide for Mac Studio.

Q: Which one is better at coding?

DeepSeek V4 Pro 1.6T dominates HumanEval, MBPP, and SWE-Bench (scores to be confirmed after official publication), inheriting the DeepSeek-Coder lineage. Llama 4 Maverick remains competitive on common Python tasks but lags on multi-file debugging. For pure coding without a VRAM budget, Qwen3-Coder-Next 80B-A3B offers the best ratio.

Q: Which inference backend is recommended for these MoE models?

SGLang and vLLM are the dominant choices for multi-GPU serving, with native support for expert parallelism. For single-user use on a Mac or workstation, llama.cpp remains the reference. Installation guides are compiled at quelllm.fr/guide/backend-inference.

Q: Can these models be fine-tuned?

Technically yes via LoRA/QLoRA, but hardware costs become prohibitive beyond the proof of concept. For DeepSeek V4 Pro, a full fine-tune easily exceeds 100 H100 nodes. Prefer SFT on DeepSeek V3.2 or Qwen 3 235B-A22B, which are much more accessible.

The showdown Llama 4 Maverick vs DeepSeek V4 Pro pits two 2026 frontier MoE philosophies against each other: a 400B-total-parameter Meta model activating a compact expert, versus a DeepSeek colossus with 1.6 trillion parameters. This comparison Llama 4 Maverick vs DeepSeek V4 Pro matters to every operator considering a local trillion-parameter deployment. We'll cover hardware specs, licenses, measured performance, use cases, and a technical FAQ.

Architecture and hardware specifications

Both models share a Mixture-of-Experts architecture, but at incomparable scales. The Llama 4 Maverick 400B totals 400 billion parameters with a design that prioritizes the activation-to-total ratio. The DeepSeek V4 Pro 1.6T pushes the count to 1,600 billion, four times the parameter count of Meta's competitor.

VRAM footprint (estimated, standard llama.cpp quantization) :

Llama 4 Maverick 400B : ~240 GB in Q4_K_M, ~300 GB in Q5_K_M, ~400 GB in Q8_0, ~800 GB in FP16 (to be confirmed depending on MoE packing)
DeepSeek V4 Pro 1.6T : ~960 GB in Q4_K_M, ~1.2 TB in Q5_K_M, ~1.6 TB in Q8_0, >3 TB in FP16

These figures illustrate the entry barrier: Maverick remains attainable on a typical 8×H100 80GB workstation or a Mac Studio M3 Ultra 512 GB with aggressive quantization. DeepSeek V4 Pro requires a multi-node infrastructure, typically 12 to 16 H100 80GB in tensor parallelism, or even a B200 cluster. The quelllm.fr VRAM guide details the calculations line by line.

As for context, both models claim 1,000,000 tokens, placing them in the same long-document usage class as GLM 5.2 753B-A40B or MiMo V2.5 Pro. In practice, actual usage beyond 200k tokens depends on the backend (vLLM, SGLang, llama.cpp) and KV-cache allocation, which quickly becomes the limiting factor.

Licenses and Redistribution Terms

The license shapes the commercial and industrial viability of a self-hosted deployment.

Llama 4 Maverick 400B : Llama 4 Community license, which imposes restrictions on derivative services beyond 700 million monthly active users, along with a "Built with Llama" attribution requirement. Full details appear in the official text published by Meta on the GitHub repository Llama.
DeepSeek V4 Pro 1.6T : MIT license, with no user-threshold clause or enhanced attribution requirement. It's the most permissive option for a closed commercial product or an internal fork.

This difference is structural. A fintech or mainstream B2C SaaS platform will prefer DeepSeek's MIT license, aligned with DeepSeek R1 671B et DeepSeek V3.2. An R&D team or business software vendor may be able to work with Llama 4 Community, as is already the case with Llama 3.1 405B Instruct et Llama 4 Scout 109B.

Performance: tokens/sec and benchmarks

Throughput figures depend heavily on the backend and batching. The values below are indicative orders of magnitude (to be confirmed) based on community feedback from HuggingFace and SGLang reports published on GitHub.

Tokens/sec in single-stream inference (estimated) :

Llama 4 Maverick 400B : 25–40 t/s on 8×H100 80GB in Q4, 8–15 t/s on Mac Studio M3 Ultra 512 GB
DeepSeek V4 Pro 1.6T : 10–18 t/s on 16×H100 80GB in Q4, Mac deployment not viable

The gap narrows in batched serving: DeepSeek V4 Pro makes better use of expert parallelism (an estimated ~7–9 active experts per token versus ~2 for Maverick, depending on the MoE profile). For a multi-user service, the cost per million tokens may favor model DeepSeek despite its footprint.

Public benchmarks (scores to be confirmed after official release) :

MMLU : Maverick ~86–88, V4 Pro ~89–91
HumanEval : Maverick ~85, V4 Pro ~90+
AIME 2024 : Maverick ~55, V4 Pro ~70+ (a marked advantage thanks to the reasoning chain)
GPQA Diamond : V4 Pro clearly ahead

For coding, we find a hierarchy consistent with DeepSeek V3 671B that already dominates Qwen3-Coder-Next 80B-A3B on SWE-Bench evaluations. The reference methodologies are described in the arXiv paper DeepSeek-V3 (2412.19437) and the HuggingFace card Llama 4.

Use cases and trade-offs

The choice between Llama 4 Maverick vs DeepSeek V4 Pro depends on the nature of the workload.

Choosing Llama 4 Maverick if :

Hardware budget capped at 250–300 GB of aggregate VRAM
Need for a mature tooling ecosystem (transformers, vLLM, and ggml are very well supported)
Multimodal workloads considered (Maverick includes a native vision tower)
Internal use with no problematic user threshold

Choose DeepSeek V4 Pro if :

Multi-node H100/B200 cluster already available
Heavy reasoning workload: mathematical proofs, multi-step agentic workflows, refactoring large codebases
Strong legal constraint on the license (requires pure MIT)
OEM distribution or white-label product

For mid-range teams, the logical compromise remains DeepSeek V4 Flash 284B or Qwen 3.5 397B-A17B, which fit in Q4 on 2 DGX nodes. The DeepSeek V4 Flash vs Qwen 3.5 397B comparison explores this trade-off.

Ecosystem and direct competition

The local trillion-parameter segment is now saturated. Besides the two main contenders, Kimi K2.6, Ring-1T et Ling 2.6 1T offer competing architectures with around 1,000B total parameters, generally under an MIT or Modified MIT license. MiMo V2.5 Pro from Xiaomi stands out with a very low activation ratio, which is interesting for server throughput.

On Meta's side, the in-house alternative remains Llama 3.1 405B Instruct, classic dense model, which still serves as a baseline in many benchmarks. For those unable to exceed 200 GB of VRAM, Mistral Large 3 675B in Q3 or Qwen 3 235B-A22B are reasonable fallback options. The complete overview is maintained on the page best open-source LLM 2026.

FAQ

Q: What is the minimum VRAM required to run DeepSeek V4 Pro 1.6T locally?

Expect approximately 960 GB of VRAM in Q4_K_M (estimated). This requires at least 12 H100 80GB GPUs in tensor parallelism, or a B200 cluster. Experimental Q2 quantization (llama.cpp i-quants) can bring it down to ~500 GB but severely degrades reasoning. No current Mac can fit the model, even in Q2.

Q: Llama 4 Maverick fit on a Mac Studio M3 Ultra 512 GB?

Yes, in Q4_K_M quantization (~240 GB), with comfortable headroom for the KV cache. Observed throughput is around 8–15 tokens/sec (estimated), depending on context length. To compare with other Mac deployments, see the LLM guide for Mac Studio.

Q: Is the Llama 4 Community license a problem for a startup?

No fewer than 700 million monthly active users. The attribution clause “Built with Llama” and the acceptable use policies remain the only real commitments. For a project seeking a purely permissive license, DeepSeek V4 Pro under MIT or Mistral Large 3 675B Apache 2.0 models are preferable.

Q: Which one is better at coding?

DeepSeek V4 Pro 1.6T dominates HumanEval, MBPP, and SWE-Bench (scores to be confirmed after official publication), inheriting from the DeepSeek-Coder family. Llama 4 Maverick remains competitive on common Python tasks but lags on multi-file debugging. For pure coding without a VRAM budget, Qwen3-Coder-Next 80B-A3B offers the best ratio.

Q: Which inference backend is recommended for these MoE models?

SGLang et vLLM are the dominant choices for multi-GPU serving, with native support for expert parallelism. For single-user use on a Mac or workstation, llama.cpp remains the benchmark. The installation guides are compiled on quelllm.fr/guide/backend-inference.

Q: Can these models be fine-tuned?

Technically yes via LoRA/QLoRA, but the hardware cost becomes prohibitive beyond the proof of concept. For DeepSeek V4 Pro, a full fine-tune easily requires more than 100 H100 nodes. Prefer SFT on DeepSeek V3.2 or Qwen 3 235B-A22B, much more accessible.

Conclusion

The verdict Llama 4 Maverick vs DeepSeek V4 Pro boils down to a budget-versus-capacity-ceiling tradeoff: Maverick is the sensible entry point to frontier MoE in 2026, DeepSeek V4 Pro is the uncompromising reasoning benchmark for those with the hardware. Refine your choice based on your available VRAM with the quelllm.fr configurator, or explore all the trillion-class alternatives in the full catalog.