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2025-12-04T05:43:39Z

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{{DISPLAYTITLE:Running large LLaMA models}}

''This conversation was exported from ChatGPT''<ref>{{cite web|title=Running large LLaMA models|url=https://chatgpt.com/share/693060a1-c630-8008-b36c-77d4e223ac3d|publisher=ChatGPT|access-date=2025-12-03}}</ref>

== gpt-5-1-instant ==
=== User: Say want to run a large llama model, how would I do that? ===
Say want to run a large llama model, how would I do that?

=== Assistant: If the aim is simply “run a large LLaMA-family model locally”, the workflow reduces to three constraints: hardware, mod… ===
If the aim is simply “run a large LLaMA-family model locally”, the workflow reduces to three constraints: hardware, model format, and runtime. The rest is plumbing.

Below is a concise, technical roadmap that assumes you want something in the 70B–405B range, and want to do it sanely rather than via YouTube-grade “just install oobabooga”.

==== 1. Hardware constraints ====

===== a. Consumer workstation (single GPU) =====

A single 24–48 GB GPU (4090, 5090, 4090D, etc.) is only realistic for:
* 7B–13B full-precision / partial-precision
* 30B with aggressive quantisation Anything above that runs, but only with offloading to CPU RAM → too slow for any latency-sensitive use.

===== b. Multi-GPU machine =====

You need NVLink or PCIe with enough bandwidth. For 70B:
* 2× 80 GB A100/H100: full-precision feasible
* 2× 48 GB RTX 6000 Ada: feasible with quantisation (Q4–Q5)

For 405B:
* 8× 80 GB (A100/H100) or more
* Or a TPU/ML cloud

===== c. CPU-only =====

Using llama.cpp with quantised weights, you can run up to ~70B on high-end desktops with ~128–192 GB RAM, but with very low throughput (tokens/s in the low single digits). Useful for experimentation, not deployment.

==== 2. Model formats ====

LLaMA-style checkpoints usually exist in three practical forms:
# Meta original format (consolidated.*.pth, params.json) Heavy, full-precision, used for training or high-end inference.
# Transformers (HuggingFace) safetensors For GPU inference and multi-GPU setups. Good with TGI / vLLM.
# GGUF (llama.cpp format) Best for local CPU/GPU hybrid and quantisation (Q2_K → Q8_K). Extremely convenient for 7B–70B.

Pick the runtime first, then pick the model format accordingly.

==== 3. Runtimes ====

===== a. vLLM =====
* Fastest for multi-GPU, continuous batching, long context.
* Use if you have strong GPUs.
* Supports most HF-format LLaMA models.
* Example:

<syntaxhighlight lang="bash">pip install vllm
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3.1-70B-Instruct \
--tensor-parallel-size 2

</syntaxhighlight>

===== b. llama.cpp =====
* Most accessible.
* CPU or mixed CPU/GPU.
* Requires GGUF weights.
* Example:

<syntaxhighlight lang="bash">./llama-cli -m Llama3-70B.Q4_K_M.gguf -c 8192 --n-gpu-layers 35

</syntaxhighlight>

===== c. Text Generation Inference (TGI) =====
* Hugging Face’s production inference stack.
* More “server-like”; supports shards, streaming.

===== d. KoboldCPP / LM Studio / Ollama =====
* Desktop-friendly abstraction layers over llama.cpp.
* Suitable for consumer-grade GPUs and CPU offloading.

===== e. Direct PyTorch =====
* Only if you genuinely want to modify inference code.
* Requires large GPUs. Minimal quantisation options.

==== 4. Step-by-step scenarios ====

===== Scenario A: You have a single 4090 and want a 70B model =====
# Download a GGUF Q4_K_M quantised 70B.
# Install llama.cpp.
# Run with small number of GPU layers offloaded:

<syntaxhighlight lang="bash">./llama-cli -m ./Llama-3.1-70B-Q4_K_M.gguf --n-gpu-layers 40 --ctx-size 4096

</syntaxhighlight>

Expect ~4–10 tokens/s, depending on quantisation.

===== Scenario B: You have 2×80 GB H100 and want 70B full precision =====
# Install vLLM.
# Pull HF weights in bf16.
# Run distributed:

<syntaxhighlight lang="bash">python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3.1-70B-Instruct \
--tensor-parallel-size 2

</syntaxhighlight>

You get 150–250 tokens/s.

===== Scenario C: You only have CPU but want to experiment with 70B =====
# Download a Q2_K or Q3_K GGUF.
# Run llama.cpp CPU-only:

<syntaxhighlight lang="bash">./llama-cli -m Llama-3.1-70B.Q2_K.gguf -t 14

</syntaxhighlight>

You get ~1–2 tokens/s.

==== 5. What “large” means in 2025 ====
* 7B–13B: fully local on laptop
* 30B–70B: local on gaming GPU or beefy desktop
* 110B–405B: realistically only multi-GPU or cloud
* >400B: cluster-only

==== 6. Latency & throughput expectations ====

Approximate for llama.cpp (GGUF):

| Model | GPU | Quant | Speed |
| ----- | ------------ | ----- | ------------ |
| 13B | 4090 | Q4_K | 80–120 tok/s |
| 34B | 4090 | Q4_K | 20–35 tok/s |
| 70B | 4090 | Q4_K | 4–10 tok/s |
| 70B | CPU | Q4_K | 1–2 tok/s |
| 70B | 2×80 GB H100 | bf16 | 200+ tok/s |

==== If you want, I can give you: ====
* a minimal reproducible setup for your exact hardware;
* a comparison of 70B quantised vs 405B quantised on your machine;
* a containerised setup (Dockerfile + compose) for llama.cpp or vLLM;
* a “what should I realistically run with your RAM/GPU?” calculator.

Tell me your hardware, and I’ll tailor the steps.

== References ==
<references />

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