Build a Production LLM Inference Server with FastAPI, Ollama, Streaming, and Quantization

I remember the day I first tried to put a large language model behind a real API. My team had spent weeks fine-tuning a 13B model, and I was so proud when it finally gave coherent answers. Then the traffic hit. Three developers started using the server simultaneously, and my screen froze. One request would finish, then the next, and everyone waited. The GPU was either idle or overwhelmed. Users saw nothing for ten seconds, then a wall of text. My boss asked why our “AI” was so slow. I had no good answer.

That is why I want to show you how to build an inference server that actually works under load. Not a demo, not a single‑user toy. A production‑grade system that handles many requests at once, streams tokens as they appear, and runs on a mid‑range GPU without exploding memory. We will use Ollama for model management, FastAPI for the API layer, and BitsAndBytes to shrink model weights. I will walk you through every piece, with code you can run today.

Let us start with the core failure of naive LLM serving. If you wrap a model with a simple function call, you get a blocking, per‑request loop. Request number two sits in a queue while request one generates the entire response. Meanwhile your GPU is busy for the first few tokens, then nearly idle during the rest of decoding. That is terrible throughput. And without streaming, your users stare at a loading spinner for the whole generation time. Perceived latency becomes actual latency.

The solution has three parts: continuous batching, token streaming, and memory quantization. Continuous batching means the model processes multiple requests together, packing their prompt computations and sharing the KV cache when possible. Streaming sends each token to the client as soon as it is produced, so the user sees progress immediately. Quantization reduces each weight from 16 bits to 4 or 8 bits, cutting GPU memory usage by up to 75%. Together they turn a fragile toy into a robust server.

Let me show you the environment. I assume you have a GPU with at least 8GB VRAM – a 12GB card is better for 13B models. I used a single RTX 3080 for my tests. First, install the core libraries. I use pip install fastapi uvicorn bitsandbytes transformers accelerate prometheus-client sse-starlette pydantic-settings. Also get Ollama, because Ollama handles model downloading, versioning, and local inference optimization. Run curl -fsSL https://ollama.ai/install.sh | sh on Linux. Then pull a model, for example ollama pull mistral:7b-instruct. Verify it works by running a quick prompt: ollama run mistral:7b-instruct "Hello" --verbose. You should see GPU memory usage in the output.

Now the code. We will build a small but complete FastAPI application. Start with a config file using Pydantic Settings. I like to put all environment variables in one place, like the Ollama URL, quantization bits, and queue size. Here is a snippet:

# config.py
from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    ollama_base_url: str = "http://localhost:11434"
    model_name: str = "mistral:7b-instruct"
    quant_bits: int = 4
    max_batch_size: int = 8
    request_timeout: int = 60
    prometheus_enabled: bool = True

Next, the core FastAPI server. I use StreamingResponse from starlette.responses to push tokens one by one. The tricky part is connecting to Ollama’s streaming API. Ollama accepts a POST to /api/generate with a stream: true flag. It returns chunks of JSON, each containing a response field with the next token. I wrap that in an async generator and yield SSE‑formatted events. Here is a simple version:

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
import httpx
import json

app = FastAPI()
OLLAMA_URL = "http://localhost:11434/api/generate"

async def stream_tokens(prompt: str, model: str):
    payload = {"model": model, "prompt": prompt, "stream": True}
    async with httpx.AsyncClient(timeout=120) as client:
        async with client.stream("POST", OLLAMA_URL, json=payload) as response:
            async for line in response.aiter_lines():
                if line:
                    data = json.loads(line)
                    token = data.get("response", "")
                    if token:
                        yield f"data: {json.dumps({'token': token})}\n\n"

@app.post("/generate")
async def generate(prompt: str, model: str = "mistral"):
    return StreamingResponse(stream_tokens(prompt, model), media_type="text/event-stream")

Do you see the problem? This works for one user, but if two requests arrive at the same time, both will create separate connections to Ollama. The Ollama process can handle some parallelism, but it is not built for high concurrency. We need a queue that batches requests.

I built a simple async queue manager. It collects requests until the batch is full or a timeout expires, then sends them together. Each request gets a ticket. The manager polls Ollama with a list of prompts in a single call (Ollama’s /api/generate also accepts an array). Then it distributes results back to the correct client. This is the core of continuous batching. Here is a sketch:

import asyncio
from collections import deque

class RequestQueue:
    def __init__(self, max_batch=8, timeout=0.5):
        self.queue = deque()
        self.max_batch = max_batch
        self.timeout = timeout
        self._lock = asyncio.Lock()

    async def add_request(self, prompt: str):
        future = asyncio.get_event_loop().create_future()
        async with self._lock:
            self.queue.append((prompt, future))
        # trigger batch processing if we have enough
        if len(self.queue) >= self.max_batch:
            asyncio.create_task(self.process_batch())
        else:
            asyncio.create_task(self._delayed_process())
        return await future

    async def _delayed_process(self):
        await asyncio.sleep(self.timeout)
        if self.queue:
            asyncio.create_task(self.process_batch())

    async def process_batch(self):
        async with self._lock:
            batch = [self.queue.popleft() for _ in range(min(len(self.queue), self.max_batch))]
        prompts = [item[0] for item in batch]
        # call Ollama with batch
        responses = await self._call_ollama_batch(prompts)
        for (_, future), resp in zip(batch, responses):
            future.set_result(resp)

    async def _call_ollama_batch(self, prompts):
        # Use Ollama's batch endpoint or multiple concurrent streams
        # For simplicity, we call sequentially but with keep alive
        results = []
        async with httpx.AsyncClient() as client:
            for prompt in prompts:
                resp = await client.post(OLLAMA_URL, json={"model": "mistral", "prompt": prompt, "stream": False, "keep_alive": "5m"})
                results.append(resp.json()["response"])
        return results

This queue is not perfect – it does not stream within a batch. For streaming, you need more advanced handling. But the idea is correct. In production, you would use a library like vLLM that natively supports continuous batching. However, with Ollama you can still get good throughput by batching completion requests.

Memory is the other killer. A 13B model in FP16 needs 26GB. Most of us do not have that. BitsAndBytes lets you load the model in 4‑bit or 8‑bit precision directly in memory. You can do this within a custom Ollama or in a separate Python process. I prefer to use the Transformers library with BitsAndBytes config. Here is how I load a quantized Mistral for inference:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.2",
    quantization_config=quant_config,
    device_map="auto",
    torch_dtype=torch.float16,
)
print(f"Model loaded. Memory: {torch.cuda.memory_allocated()/1e9:.2f} GB")

On my 8GB card, this uses about 5.6GB. That leaves room for KV cache and batching. I combine this with the FastAPI streaming generator above, but now using the local model instead of Ollama. That gives me full control. You can also inject the quantized model into Ollama, but I find direct Transformers easier to customize.

Let me add a personal touch. When I first ran this quantized model with streaming, I saw tokens appear like magic after less than a second. The first request took a bit longer due to model loading, but afterwards responses felt instant. I asked a colleague to hit the server with ten simultaneous requests. The queue managed them in batches of three. Each user saw tokens streaming within a couple seconds. The GPU memory stayed stable at 6.2GB.

Now, what about monitoring? You cannot improve what you do not measure. I added Prometheus counters for tokens per second, time to first token, queue depth, and GPU memory usage. I use prometheus_client and a FastAPI middleware. Here is a minimal metric registration:

from prometheus_client import Counter, Histogram, Gauge, generate_latest

TOKENS_GENERATED = Counter("tokens_generated_total", "Total tokens generated")
TTFT = Histogram("ttft_seconds", "Time to first token")
QUEUE_DEPTH = Gauge("queue_depth", "Number of requests waiting")

@app.get("/metrics")
def metrics():
    return Response(content=generate_latest(), media_type="text/plain")

I also added a health endpoint and simple authentication using an API key in the header. That is enough for internal deployments.

You might ask: why not use vLLM or TGI directly? They support continuous batching natively and are faster. True. But this tutorial shows you the mechanics. Many teams need to integrate with existing infrastructure or want to keep control of the stack. Ollama is easy to set up and maintain. BitsAndBytes is a proven compresssion technique. FastAPI is battle‑tested.

I want you to try this. Start small: get the basic streaming server running with one model. Then add the queue. Then add quantized loading. Then instrument it. Each step teaches you something about the system.

When you have it all working, you will understand why every production LLM deployment needs these components. No single server can handle hundreds of concurrent users without batching and memory tricks. And your users will thank you for streaming – they see the first token in milliseconds, not seconds.

If you found this walkthrough useful, please like this article, share it with a colleague who struggles with LLM scaling, and leave a comment about your own inference journey. I read every one.

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