Title: Openai/693060a1-c630-8008-b36c-77d4e223ac3d - freem

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## gpt-5-1-instant[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=1 "Edit section: gpt-5-1-instant")

### User: Say want to run a large llama model, how would I do that?[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=2 "Edit section: 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…[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=3 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=4 "Edit section: 1. Hardware constraints")

##### a. Consumer workstation (single GPU)[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=5 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=6 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=7 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=8 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=9 "Edit section: 3. Runtimes")

##### a. vLLM[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=10 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=11 "Edit section: 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)[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=12 "Edit section: c. Text Generation Inference (TGI)")

*   Hugging Face’s production inference stack.
*   More “server-like”; supports shards, streaming.

##### d. KoboldCPP / LM Studio / Ollama[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=13 "Edit section: d. KoboldCPP / LM Studio / Ollama")

*   Desktop-friendly abstraction layers over llama.cpp.
*   Suitable for consumer-grade GPUs and CPU offloading.

##### e. Direct PyTorch[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=14 "Edit section: e. Direct PyTorch")

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

#### 4. Step-by-step scenarios[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=15 "Edit section: 4. Step-by-step scenarios")

##### Scenario A: You have a single 4090 and want a 70B model[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=16 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=17 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=18 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=19 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=20 "Edit section: 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:[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=21 "Edit section: 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[edit](https://freemwiki.com/index.php?title=Openai/693060a1-c630-8008-b36c-77d4e223ac3d&action=edit&section=22 "Edit section: References")

1.   [↑](https://freemwiki.com/index.php?mobileaction=toggle_view_mobile&title=Openai%2F693060a1-c630-8008-b36c-77d4e223ac3d#cite_ref-1)["Running large LLaMA models"](https://chatgpt.com/share/693060a1-c630-8008-b36c-77d4e223ac3d). ChatGPT. Retrieved 2025-12-03.