Local 70B LLM in 2026: DeepSeek-R1 vs Llama 3.3 vs Nemotron — Which to Choose?
Detailed comparison of the top 3 open-source 70B LLMs in 2026. Analyzing tok/s benchmarks, reasoning quality, and RAM consumption. Find the best model for your specific use case.
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.
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.