> ## Documentation Index > Fetch the complete documentation index at: https://docs.baseten.co/llms.txt > Use this file to discover all available pages before exploring further. # Llama 3.3 > Meta's Llama 3.3 70B instruction-tuned model.
Tool calling Long context
Meta's Llama 3.3 70B instruction-tuned model. Both presets run on H100:4 under vLLM from NVIDIA's FP8 checkpoint: one tuned for low time-to-first-token, one for total token throughput. ## Setup Install the Baseten CLI and sign in, then install the OpenAI SDK. **Install and sign in to Baseten** ```bash Terminal theme={"system"} brew tap basetenlabs/baseten brew install baseten ``` Download and extract the binary, then move `baseten.exe` to a directory on your `PATH`: ```powershell Terminal theme={"system"} Invoke-WebRequest ` https://github.com/basetenlabs/baseten-cli/releases/download/v0.4.0/baseten_0.4.0_windows_amd64.zip ` -OutFile baseten.zip; Expand-Archive -Force baseten.zip . ``` For other platforms or a specific version, see the [Baseten CLI install reference](/reference/cli/baseten/overview#install). ```sh theme={"system"} baseten auth login ``` **Install the OpenAI SDK** ```sh theme={"system"} uv pip install openai ``` Prefer not to install? Sign in with `uvx truss login --browser` and deploy with `uvx truss push`. This variant ships in 2 presets tuned for different goals: **Latency** for lowest time-to-first-token, and **Throughput** for highest tokens per second. Pick the tab that matches your workload. This preset serves Llama 3.3 70B Instruct on H100:4 under vLLM with FP8 weights and tensor parallelism. It targets low time-to-first-token on the 70B chat model. H100 × 4 vLLM 0.26.0 128K 128 ## Write the config Create and move into the project directory: ```sh theme={"system"} mkdir llama-3.3-70b-instruct-latency && cd llama-3.3-70b-instruct-latency ``` Then create a file named `config.yaml` and paste the following: ```yaml config.yaml theme={"system"} model_name: "model:llama-3.3-70b-instruct preset:latency" model_metadata: repo_id: nvidia/Llama-3.3-70B-Instruct-FP8 tags: - openai-compatible - vllm example_model_input: stream: true model: nvidia/Llama-3.3-70B-Instruct-FP8 messages: - role: user content: Tell me everything you know about optimized inference. max_tokens: 512 temperature: 0.5 base_image: image: vllm/vllm-openai:v0.26.0 docker_server: start_command: >- vllm serve /app/model_cache/llama-3-3-70b-instruct --served-model-name nvidia/Llama-3.3-70B-Instruct-FP8 --host 0.0.0.0 --port 8000 --tensor-parallel-size 4 --distributed-executor-backend mp --max-model-len 131072 --max-num-seqs 128 --max-num-batched-tokens 8192 --enable-chunked-prefill --enable-prefix-caching --gpu-memory-utilization 0.90 --load-format runai_streamer readiness_endpoint: /health liveness_endpoint: /health predict_endpoint: /v1/chat/completions server_port: 8000 secrets: hf_access_token: null weights: - source: hf://nvidia/Llama-3.3-70B-Instruct-FP8@main allow_patterns: - "*.safetensors" - "*.json" - "*.model" - tokenizer.model - "*.tiktoken" - "*.jinja" mount_location: /app/model_cache/llama-3-3-70b-instruct ignore_patterns: - original/* - "*.pth" auth_secret_name: hf_access_token resources: cpu: "4" memory: 40Gi use_gpu: true accelerator: H100:4 runtime: predict_concurrency: 128 streaming_read_timeout: 60 health_checks: startup_threshold_seconds: 1800 restart_threshold_seconds: 1200 stop_traffic_threshold_seconds: 120 environment_variables: VLLM_LOGGING_LEVEL: INFO VLLM_ENGINE_READY_TIMEOUT_S: "3600" ``` This config runs Llama 3.3 70B Instruct on four H100 GPUs with vLLM, loading FP8 weights from `nvidia/Llama-3.3-70B-Instruct-FP8` and sharding them across the four ranks. The server holds the batch to 128 sequences and 8192 batched tokens per scheduler step, and chunked prefill keeps long prompts from stalling the requests already decoding. ## Flags The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior: | Flag | Value | What it does | | -------------------------------- | ---------------- | -------------------------------------------------------------------------------------------------------------------- | | `--tensor-parallel-size` | `4` | Number of GPUs to shard the model across. | | `--distributed-executor-backend` | `mp` | How vLLM coordinates tensor-parallel workers across processes. **mp:** Python multiprocessing (single-node default). | | `--max-model-len` | `131072` | Maximum context length (tokens) the server accepts per request. | | `--max-num-seqs` | `128` | Maximum number of concurrent sequences in the batch. | | `--max-num-batched-tokens` | `8192` | Maximum total tokens processed per scheduler step. | | `--enable-chunked-prefill` | (no value) | Process long prompts in chunks so decode requests keep running. | | `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. | | `--gpu-memory-utilization` | `0.90` | Fraction of GPU memory vLLM may use for weights and KV cache. | | `--load-format` | `runai_streamer` | Weight loading backend. **runai\_streamer:** Stream weights from object storage without materializing to disk. | ## Deploy Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it: ```sh Baseten CLI theme={"system"} baseten model push ``` ```sh Truss CLI theme={"system"} uvx truss push ``` You should see output similar to: ```output theme={"system"} Pushing model "llama-3.3-70b-instruct-latency"... Uploading model... Uploaded model in 0s ✨ Model llama-3.3-70b-instruct-latency was successfully pushed ✨ Model: llama-3.3-70b-instruct-latency (abc1d2ef) Deployment: xyz123 Environment: production 🪵 View logs: deployment: baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123 environment: baseten model environment logs --model-id abc1d2ef --environment production (once deployed) app: https://app.baseten.co/models/abc1d2ef/logs/xyz123 🚀 Invoke your model: URL: https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict CLI: baseten model predict --model-id abc1d2ef ``` `baseten model push` prints your **model ID** (`abc1d2ef` in the example). The examples below use it wherever you see `{model_id}`, and read your API key from the `BASETEN_API_KEY` environment variable. ## Call the model Your deployment serves an OpenAI-compatible API. Now call your deployment to run inference: ```python main.py theme={"system"} import os from openai import OpenAI client = OpenAI( api_key=os.environ["BASETEN_API_KEY"], base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1", ) response = client.chat.completions.create( model="nvidia/Llama-3.3-70B-Instruct-FP8", messages=[ {"role": "user", "content": "What is machine learning?"} ], ) print(response.choices[0].message.content) ``` ```sh theme={"system"} curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $BASETEN_API_KEY" \ -d '{ "model": "nvidia/Llama-3.3-70B-Instruct-FP8", "messages": [ {"role": "user", "content": "What is machine learning?"} ] }' ``` This preset serves Llama 3.3 70B Instruct on H100:4 under vLLM with FP8 weights, optimized for total token throughput on long-context workloads. H100 × 4 vLLM 0.26.0 128K 256 ## Write the config Create and move into the project directory: ```sh theme={"system"} mkdir llama-3.3-70b-instruct-throughput && cd llama-3.3-70b-instruct-throughput ``` Then create a file named `config.yaml` and paste the following: ```yaml config.yaml theme={"system"} ######################################################## # Throughput preset for Llama 3.3 70B Instruct (FP8), H100:4, vLLM. # # Tuned for high total token throughput on long-context workloads. # Key throughput levers compared with the latency preset: # gpu-memory-utilization 0.95 and max-num-seqs 256 for more KV cache headroom # and larger batches, plus prefix caching for shared prefixes. FP8 (ModelOpt) # weights and fp8 KV cache are auto-detected from the checkpoint. # Raising max-num-batched-tokens beyond 16384 added no further throughput. ######################################################## model_name: "model:llama-3.3-70b-instruct preset:throughput" model_metadata: repo_id: nvidia/Llama-3.3-70B-Instruct-FP8 tags: - openai-compatible - vllm example_model_input: stream: true model: nvidia/Llama-3.3-70B-Instruct-FP8 messages: - role: user content: Tell me everything you know about optimized inference. max_tokens: 512 temperature: 0.5 base_image: image: vllm/vllm-openai:v0.26.0 docker_server: start_command: >- vllm serve /app/model_cache/llama-3-3-70b-instruct --served-model-name nvidia/Llama-3.3-70B-Instruct-FP8 --host 0.0.0.0 --port 8000 --tensor-parallel-size 4 --distributed-executor-backend mp --max-model-len 131072 --max-num-seqs 256 --max-num-batched-tokens 16384 --enable-chunked-prefill --enable-prefix-caching --gpu-memory-utilization 0.95 readiness_endpoint: /health liveness_endpoint: /health predict_endpoint: /v1/chat/completions server_port: 8000 weights: - source: hf://nvidia/Llama-3.3-70B-Instruct-FP8@main allow_patterns: - "*.safetensors" - "*.json" - "*.model" - tokenizer.model - "*.tiktoken" - "*.jinja" mount_location: /app/model_cache/llama-3-3-70b-instruct ignore_patterns: - original/* - "*.pth" auth_secret_name: hf_access_token secrets: hf_access_token: null resources: cpu: "4" memory: 40Gi use_gpu: true accelerator: H100:4 runtime: predict_concurrency: 256 streaming_read_timeout: 60 health_checks: restart_check_delay_seconds: 1800 restart_threshold_seconds: 1200 stop_traffic_threshold_seconds: 120 environment_variables: VLLM_LOGGING_LEVEL: INFO VLLM_ENGINE_READY_TIMEOUT_S: "3600" ``` This config gives vLLM more room to batch than the latency preset does: 256 concurrent sequences, 16384 batched tokens per scheduler step, and 95% of each H100's memory for weights and KV cache. Prefix caching keeps the shared portion of repeated prompts resident, which pays off most when many requests carry the same long system prompt. ## Flags The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior: | Flag | Value | What it does | | -------------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------- | | `--tensor-parallel-size` | `4` | Number of GPUs to shard the model across. | | `--distributed-executor-backend` | `mp` | How vLLM coordinates tensor-parallel workers across processes. **mp:** Python multiprocessing (single-node default). | | `--max-model-len` | `131072` | Maximum context length (tokens) the server accepts per request. | | `--max-num-seqs` | `256` | Maximum number of concurrent sequences in the batch. | | `--max-num-batched-tokens` | `16384` | Maximum total tokens processed per scheduler step. | | `--enable-chunked-prefill` | (no value) | Process long prompts in chunks so decode requests keep running. | | `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. | | `--gpu-memory-utilization` | `0.95` | Fraction of GPU memory vLLM may use for weights and KV cache. | ## Deploy Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it: ```sh Baseten CLI theme={"system"} baseten model push ``` ```sh Truss CLI theme={"system"} uvx truss push ``` You should see output similar to: ```output theme={"system"} Pushing model "llama-3.3-70b-instruct-throughput"... Uploading model... Uploaded model in 0s ✨ Model llama-3.3-70b-instruct-throughput was successfully pushed ✨ Model: llama-3.3-70b-instruct-throughput (abc1d2ef) Deployment: xyz123 Environment: production 🪵 View logs: deployment: baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123 environment: baseten model environment logs --model-id abc1d2ef --environment production (once deployed) app: https://app.baseten.co/models/abc1d2ef/logs/xyz123 🚀 Invoke your model: URL: https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict CLI: baseten model predict --model-id abc1d2ef ``` `baseten model push` prints your **model ID** (`abc1d2ef` in the example). The examples below use it wherever you see `{model_id}`, and read your API key from the `BASETEN_API_KEY` environment variable. ## Call the model Your deployment serves an OpenAI-compatible API. Now call your deployment to run inference: ```python main.py theme={"system"} import os from openai import OpenAI client = OpenAI( api_key=os.environ["BASETEN_API_KEY"], base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1", ) response = client.chat.completions.create( model="nvidia/Llama-3.3-70B-Instruct-FP8", messages=[ {"role": "user", "content": "What is machine learning?"} ], ) print(response.choices[0].message.content) ``` ```sh theme={"system"} curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $BASETEN_API_KEY" \ -d '{ "model": "nvidia/Llama-3.3-70B-Instruct-FP8", "messages": [ {"role": "user", "content": "What is machine learning?"} ] }' ``` ## Next steps Endpoint anatomy, authentication, and sync versus async inference Scale replicas with traffic, including scale to zero