Openai/693060a1-c630-8008-b36c-77d4e223ac3d

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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:

1. Meta original format (consolidated.*.pth, params.json) Heavy, full-precision, used for training or high-end inference.
2. Transformers (HuggingFace) safetensors For GPU inference and multi-GPU setups. Good with TGI / vLLM.
3. 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:

pip install vllm
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 2

b. llama.cpp

• Most accessible.
• CPU or mixed CPU/GPU.
• Requires GGUF weights.
• Example:

./llama-cli -m Llama3-70B.Q4_K_M.gguf -c 8192 --n-gpu-layers 35

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

1. Download a GGUF Q4_K_M quantised 70B.
2. Install llama.cpp.
3. Run with small number of GPU layers offloaded:

./llama-cli -m ./Llama-3.1-70B-Q4_K_M.gguf --n-gpu-layers 40 --ctx-size 4096

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

Scenario B: You have 2×80 GB H100 and want 70B full precision

1. Install vLLM.
2. Pull HF weights in bf16.
3. Run distributed:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 2

You get 150–250 tokens/s.

Scenario C: You only have CPU but want to experiment with 70B

1. Download a Q2_K or Q3_K GGUF.
2. Run llama.cpp CPU-only:

./llama-cli -m Llama-3.1-70B.Q2_K.gguf -t 14

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.