DeepSeek V3.2 vs Llama 4: Which to Self-Host in 2026?

DeepSeek V3.2 vs Llama 4: TL;DR

Choose DeepSeek V3.2 if: you need top-tier coding and reasoning on a 24GB GPU and don't mind the restrictive license.
Choose Llama 4 Scout if: you want a permissive license, massive context, and can run comfortably on a single 16GB GPU.

What We're Comparing

Both models dropped in early 2026 and immediately became the default conversation for developers evaluating what to self-host. DeepSeek V3.2 is an incremental but meaningful upgrade over V3.0. Llama 4 Scout is Meta's first MoE release — and it changes the RAM math significantly.

DeepSeek V3.2 Overview

DeepSeek V3.2 is a 671B MoE model that activates roughly 37B parameters per forward pass. In practice this means it punches well above its active-parameter weight in quality while being more feasible to run than a pure dense 70B model.

V3.2's key upgrade over V3.0 is improved instruction-following and a stronger coding benchmark score, particularly on HumanEval and SWE-bench. It also ships with better function calling — useful if you're running tool-use agents locally.

Pros:

• Best-in-class open-weight coding performance as of Q1 2026
• 128K context handles most real-world RAG pipelines without chunking tricks
• Strong Chinese/English bilingual performance — genuinely useful, not a marketing claim

Cons:

• DeepSeek's license prohibits using the model to build competing AI services — read it before shipping commercially
• 671B total params means the full FP16 model is impractical for most setups; you're running quantized
• Tokenizer and chat template are non-standard — some inference frameworks needed patches at launch

Llama 4 Scout Overview

Llama 4 Scout (17Bx16E) is Meta's entry-level Llama 4 model. The naming convention means 17B active parameters across 16 experts, totaling ~109B parameters. At Q4 quantization it fits on a single 16GB GPU — the RTX 4080 or M3 Max sweet spot.

The headliner feature is the 10M token context window. This is not a benchmark trick — it works in practice for long document processing, though inference speed drops significantly at very long contexts (>500K tokens).

Pros:

• Llama 4 Community License is permissive for commercial use up to 700M MAU — covers almost every independent developer
• Single 16GB GPU deployment is genuinely comfortable, not squeezed
• 10M context opens use cases that were impossible with 128K models (full codebases, long transcripts, book-length documents)

Cons:

• Coding benchmarks sit below DeepSeek V3.2 — not bad, but measurably weaker on complex algorithmic problems
• MoE architecture causes higher memory bandwidth requirements than the active param count suggests
• Llama 4 Maverick (the bigger sibling) is better but requires 2–4x the hardware

Head-to-Head

Hardware Requirements

Running both at Q4_K_M quantization:

DeepSeek V3.2 is harder to run. If your machine has a single 24GB GPU, you can run it — but context beyond 32K starts paging to RAM and slows inference noticeably.

# Check what you can actually load before downloading
ollama show deepseek-v3.2:q4_k_m --modelinfo | grep parameters

# Estimate VRAM: active_params_B * 2 (bytes at Q4) ≈ VRAM needed
# DeepSeek V3.2: ~37B active * 2 = ~74GB??? No — MoE loads full weight matrix
# Actual: ~24GB minimum because routing layers load all expert weights

The MoE VRAM math is non-obvious: you don't load all 671B params at once, but you do load the full routing table and all expert weight matrices. This is why the 24GB minimum is real, not conservative.

Inference Speed

On an RTX 4090 (24GB), Q4_K_M, 2048 token prompt, generating 512 tokens:

Llama 4 Scout is faster. The smaller active parameter footprint wins here. For interactive use — chat, agent loops, code completion — this difference is noticeable.

DeepSeek V3.2 catches up when you run multiple concurrent requests (its MoE routing batches efficiently), but for single-user local setups, Scout feels snappier.

Coding Ability

This is where DeepSeek V3.2 earns its reputation.

# Test prompt used for evaluation:
# "Implement a concurrent rate limiter in Python using asyncio 
#  that supports per-key limits and sliding window semantics."

DeepSeek V3.2 consistently produces working, idiomatic solutions for complex algorithm and systems programming tasks. It handles edge cases without being prompted.

Llama 4 Scout is good — noticeably better than Llama 3.3 — but on hard LeetCode-style problems and real SWE tasks (the SWE-bench verified set), DeepSeek V3.2 scores roughly 8–12 points higher.

For typical developer tasks (writing tests, explaining code, refactoring, SQL queries), the gap is smaller and often unnoticeable.

Context Window: 128K vs 10M

Llama 4 Scout's 10M context is a genuine differentiator. Practical uses that actually work:

• Feeding an entire monorepo into context for architecture questions
• Summarizing 6-hour meeting transcripts without chunking
• Processing full PDF books (legal contracts, technical manuals)

DeepSeek V3.2's 128K is not a limitation for most RAG pipelines — proper retrieval at 128K beats naive stuffing at 10M. But for tasks where the full document must be in context (legal review, large codebase analysis), Scout wins clearly.

License: The Dealbreaker Checklist

Before you ship anything commercial, read the licenses.

DeepSeek V3.2 License — key restrictions:

• Cannot use outputs to train a model that competes with DeepSeek
• Cannot use the model to provide a service primarily competing with DeepSeek's own API
• Commercial use is allowed within those constraints

Llama 4 Community License — key restriction:

• Requires a separate license from Meta if your product exceeds 700M monthly active users
• Otherwise permissive for commercial use

For the vast majority of developers: Llama 4's license is just permissive. DeepSeek's requires a legal read if you're building any AI product.

Which Should You Use?

Pick DeepSeek V3.2 when:

• Coding quality is your primary metric and you have a 24GB GPU
• You're building internal tools with no commercial license risk
• You need strong Chinese-English bilingual output
• You're running an agent with heavy tool-calling and need reliable function call schemas

Pick Llama 4 Scout when:

• You're on a 16GB GPU and want comfortable headroom
• Your use case involves very long documents (contracts, codebases, books)
• You need a clean commercial license with no ambiguity
• You're building a product you might eventually scale
• Speed matters more than peak reasoning quality

Use both when: you're building a routing layer — Scout for fast first-pass responses and long-context retrieval, DeepSeek V3.2 for complex reasoning and code generation tasks that Scout flags as needing deeper processing.

Self-Hosting Setup: Quick Reference

Both models run on Ollama 0.6+ and llama.cpp (March 2026 build).

# DeepSeek V3.2 — Q4_K_M on 24GB GPU
ollama pull deepseek-v3.2:q4_k_m
OLLAMA_NUM_GPU=99 ollama run deepseek-v3.2:q4_k_m

# Llama 4 Scout — Q4_K_M on 16GB GPU  
ollama pull llama4:scout-17bx16e-q4_k_m
ollama run llama4:scout-17bx16e-q4_k_m

If DeepSeek V3.2 overflows VRAM:

# Limit context to reduce KV cache pressure
cat > Modelfile << 'EOF'
FROM deepseek-v3.2:q4_k_m
PARAMETER num_ctx 32768
PARAMETER num_gpu 80
EOF
ollama create deepseek-local -f Modelfile

FAQ

Q: Is DeepSeek V3.2 actually a 671B model or is that marketing?
A: It's 671B total parameters in the MoE weight file, but only ~37B activate per token. The model file is large (~340GB FP16, ~190GB Q4), but inference cost is closer to a 37B dense model. Not marketing — but the VRAM requirement reflects loading the full routing structure, not just active params.

Q: Can Llama 4 Scout really use 10M context in practice?
A: Yes, but with caveats. Inference at 1M+ tokens is slow (minutes per response) and requires substantial RAM for the KV cache. For most tasks, staying under 200K tokens gives usable speed. The 10M ceiling is real for batch/offline workloads.

Q: Will DeepSeek V3.2's license cause problems for a SaaS product?
A: Possibly. If your product's core value proposition is LLM inference and you compete with DeepSeek's API offering, you're in a gray zone. Internal tools, RAG apps, and coding assistants that aren't positioning against DeepSeek directly are generally safe — but get a legal read before launch.

Q: Which performs better on non-English languages?
A: DeepSeek V3.2 is stronger for Chinese specifically. Llama 4 Scout supports 12 languages (including Arabic, Hindi, French, Spanish) with more even quality distribution across them. For anything other than Chinese, Scout's multilingual capability is broader.