Local 70B LLM in 2026: DeepSeek-R1 vs Llama 3.3 vs Nemotron — Which to Choose?

TL;DR: The 3 open-source 70B LLMs usable locally in 2026 all run at approximately 4.7 tok/s on ARM unified-memory hardware (GB10). The choice comes down to response quality, not speed: DeepSeek-R1 for long reasoning, Nemotron for general tasks, Llama 3.3 for versatility. Details below.

The 70B class has become accessible locally in 2026

Until late 2025, running a 70B LLM locally required an RTX 6000 Ada or an H100 (€8,000–15,000). In 2026, unified-memory machines like the NVIDIA GB10 (DGX Spark, ~€3,000) or Mac Studio M4 Ultra (192 GB) have made this accessible.

At 4–5 tok/s, you aren’t doing interactive chat, but you can handle:

• Long-form document summarization
• Batch classification
• Long chain-of-thought (CoT) reasoning
• Complex code generation (in the background)

So the question is no longer “is it possible” but “which 70B to choose.” I tested the three serious open-source candidates.

Pure speed: a tie

Model	eval tok/s	prompt tok/s	RAM consumed
DeepSeek-R1 70B (Q4)	4.7	218	81 GB
Llama 3.3 70B (Q4)	4.7	254	81 GB
Nemotron 70B (Q4)	4.7	260	(cache hit)

Reading: Inference speed is limited by memory bandwidth (LPDDR5x ~273 GB/s), not by the model weights. All three run at the same speed.

→ The choice depends on output quality, not speed.

Comparison by use case

1. Long multi-step reasoning (CoT) → DeepSeek-R1

DeepSeek-R1 is trained with an explicit “thinking tokens” system that breaks down reasoning step-by-step, similar to OpenAI’s o1. It is the only local 70B model in 2026 capable of solving complex problems (math, formal reasoning, logical debugging) at a level comparable to GPT-4o.

Ideal use cases:

• Complex codebase analysis (“find the bug in this interaction between 3 modules”)
• Math/logic problem solving
• Strategic decomposition into sub-tasks

Limitation: The “thinking” phase consumes 30–50% of the output budget. For a response with 200 useful tokens, expect 400–600 generated tokens.

2. Balanced general tasks → Llama 3.3

Llama 3.3 was released in late 2024 and remains the open-source “generalist” reference in 2026. It has solid multilingual capabilities (French is fine), is well-aligned, and knows when to say “I don’t know” rather than hallucinate.

Ideal use cases:

• Multilingual chat
• Content generation (articles, copy, scripts)
• Document Q&A (with RAG)
• Custom fine-tuning (largest community, most tutorials)

Limitation: Less capable in pure math than DeepSeek-R1.

3. Specialized NVIDIA-stack tasks → Nemotron 70B

Nemotron 70B is a fine-tune of Llama 3.3 by NVIDIA, optimized for enterprise RAG and tool-use agents. Performance is marginally similar to Llama 3.3 on general benchmarks, but significantly superior in tool calling.

Ideal use cases:

• Agents with tool use (functions, APIs)
• Enterprise RAG (good embeddings + reasoning)
• Structured extraction pipelines

Limitation: Perceived as more “rigid” qualitatively compared to Llama 3.3 on free-form creative tasks.

The RAM consumption factor

DeepSeek-R1 and Llama 3.3 consume 81 GB in Q4 (4-bit). Nemotron, being a fine-tune of Llama 3.3 with identical architecture, consumes the same amount.

→ On a GB10 with 121 GB unified memory, you can run only ONE 70B model at a time (leaving a 40 GB margin for the OS and workloads).

Avoid: Trying Q8 (160 GB) or FP16 (320 GB); it won’t fit in memory.

Final recommendation by profile

Your profile	Recommended Model
Developer wanting a code assistant + complex analysis	DeepSeek-R1 70B
Sysadmin / hosting / enterprise RAG / agents	Nemotron 70B
Versatile: one model for everything	Llama 3.3 70B
Want to try agentic tool calling	Nemotron 70B

Required hardware in 2026

To run one of these 70B models locally at 4–5 tok/s:

• Budget option: NVIDIA DGX Spark / MSI EdgeXpert ARM64 GB10 (~€3,000 new) → what I tested
• High RAM option: Mac Studio M4 Max 64 GB or M4 Ultra 192 GB (~€3,500–7,000)
• Workstation option: 2× RTX 4090 24 GB via tensor parallelism (~€4,000 + complex setup)

If you prefer renting over buying, some hourly GPU VPS providers allow you to test these models without investing:

• Hostinger offers VPS plans with H100s available for hourly rental for intensive testing (affiliate link)
• Vast.ai, Runpod, and Lambda Labs are non-affiliated alternatives if you prefer

FAQ

Which quantized format to choose? Q4_K_M is the 2026 sweet spot. Q5_K_M if you have the GB available. Q8 and FP16 are a waste for local 70B models.

What about Qwen 2.5 72B? Also a good model (Chinese Alibaba), excellent at coding. I will cover it in a dedicated article. Generally comparable to Llama 3.3 as a generalist.

What about 70B+ MoE models? MoE 100–200B models with ~10–20B active parameters are the next wave. Qwen3 235B-A22B (MoE mode), Mixtral 8x22B. I cover them in a separate article on MoE once Ollama tools support the new architectures.

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Affiliate disclosure

This article contains affiliate links (notably Hostinger). If you click and make a purchase, I earn a commission at no extra cost to you. See full disclosure. Rankings and recommendations remain based exclusively on measured benchmarks.

Article written on May 28, 2026.