Groq vs SGLang: which should you use for LLM inference?
Use Groq if you need the fastest possible latency and can accept closed-source proprietary hardware. Use SGLang if you want open-source flexibility, multi-model serving, or control over your deployment stack.
VERDICT
Side-by-side comparison
| Dimension | Groq | SGLang | Winner |
|---|---|---|---|
| Latency (7B model) | ~100-150ms E2E | ~200-400ms (A100) | Groq |
| Throughput | ~20-30 req/s (shared) | ~500-1000 tok/s (single GPU) | SGLang |
| Hardware | Proprietary LPU | NVIDIA/AMD GPU (standard) | SGLang |
| Cost per 1M tokens | $0.35-0.70 (API) | $0.05-0.15 (self-hosted) | SGLang |
| Multi-model support | Single model at a time | Multiple models, LoRA, vLLM compatible | SGLang |
| Open source | No (closed API) | Yes (Apache 2.0) | SGLang |
| Installation complexity | API key only | pip install + GPU setup | Groq |
| Deployment model | Cloud API only | Self-hosted or managed | SGLang |
| Supported models | Llama 2, Mixtral, Qwen | 100+ HuggingFace models | SGLang |
| Custom kernel optimization | Groq-specific LLMOps | Custom LoRA, speculative decoding | Tie |
Performance benchmarks
Time to first token (7B chat model, single request)
Groq's LPU architecture eliminates memory bottleneck; SGLang adds GPU memory latency but still production-viable
Per-token latency sustained (throughput)
Groq is API-shared; SGLang shows higher throughput in dedicated mode but measured on user's own hardware
Cost per 1 million input tokens
Groq API convenience premium; SGLang requires GPU cost absorption but scales cheaper at volume
Model serving latency (batch=1, Llama-2-70B)
Groq's strength is low-latency single/small-batch inference; SGLang optimized for throughput
When to use each
- ✓ Real-time agent reasoning or interactive chatbots where <200ms latency is critical (legal advisors, financial traders, live customer support)
- ✓ API-first deployment where you want zero infrastructure: just an API key and no GPU management
- ✓ Multi-tenant SaaS where latency consistency matters more than total throughput or cost efficiency
- ✓ Proof-of-concept or low-volume production (<10k daily requests) where managed service cost is acceptable
- ✓ When you need LPU specialization for specific model architectures and accept vendor lock-in
- ✓ Self-hosted production serving with 100+ concurrent requests where throughput and cost matter (e.g., content generation, summarization platforms)
- ✓ Multi-model inference (Llama, Mixtral, Qwen, Llava) served from a single endpoint with dynamic routing
- ✓ Fine-tuned models or LoRA adapters where you need custom model variants without API vendor support
- ✓ Edge or on-premise deployment where cloud API is not an option or has compliance/data residency constraints
- ✓ Cost-sensitive workloads at scale (millions of tokens/month) where self-hosting saves 80-90% vs Groq API pricing
Common misconceptions
Groq
Groq is cheaper because it's specialized hardware.
Groq charges API pricing (~$0.35-0.70 per million input tokens) that is 5-10x the self-hosted cost of SGLang on an A100. The speed advantage doesn't translate to cost savings unless latency directly monetizes your product.
Groq can serve multiple models simultaneously.
Groq GroqCloud serves one model at a time per API call. If you need multi-model inference (Llama + Mistral + Llava), you must swap models or run separate API calls, adding latency and cost. SGLang handles this natively.
Groq works with any model you fine-tune.
Groq only supports pre-optimized models (Llama 2, Mixtral, Qwen). Custom fine-tunes or LoRA adapters don't work without Groq's explicit support and optimization: a months-long process. SGLang supports any HuggingFace model immediately.
SGLang
SGLang is as fast as Groq.
SGLang on A100 has ~2-3x higher latency than Groq for first token (~200-250ms vs 50-80ms). For real-time interactive use cases, this difference is noticeable. SGLang wins on throughput, not latency.
SGLang is production-ready out of the box.
SGLang requires GPU setup, CUDA/PyTorch tuning, and infrastructure management. You are responsible for monitoring, scaling, and failover. Groq handles this for you as a managed API.
SGLang replaces your inference framework entirely.
SGLang is compatible with vLLM and other inference backends but has a learning curve: you must understand LLM serving fundamentals (batching, quantization, memory management). For teams without ML infrastructure experience, Groq's API is simpler to deploy.
Code examples
Task: Run a single inference call with a chat prompt and return the generated text.
import os
from groq import Groq
# Groq API key from environment
client = Groq(api_key=os.environ["GROQ_API_KEY"])
# Single inference call: Groq-specific proprietary hardware
message = client.chat.completions.create(
model="mixtral-8x7b-32768", # Pre-optimized on Groq LPU
messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}],
max_tokens=100,
temperature=0.7,
)
print(message.choices[0].message.content)Groq uses a standard OpenAI-compatible API, making adoption simple, but only supports Groq-optimized models and executes on their proprietary LPU hardware: no local control.
import os
from sglang import Engine, SamplingParams
# Initialize SGLang engine with a HuggingFace model on your GPU
engine = Engine(model_name="meta-llama/Llama-2-7b-chat-hf")
# Single inference call: uses your local GPU hardware
sampling_params = SamplingParams(temperature=0.7, max_new_tokens=100)
outputs = engine.generate(
prompts=["Explain quantum computing in one sentence."],
sampling_params=sampling_params,
)
print(outputs[0].text)SGLang runs inference on your own hardware (standard NVIDIA/AMD GPU), giving you full control over model choice, deployment, and scaling: but requires GPU setup and maintenance.
Migration path
- Switching from Groq to SGLang:
- Replace groq Python client with sglang: `pip uninstall groq && pip install sglang`.
- Replace model names: Groq's 'mixtral-8x7b-32768' → SGLang's 'meta-llama/Llama-2-7b-chat-hf' (any HuggingFace model).
- Replace API call: Groq's client.chat.completions.create() → SGLang's engine.generate() with SamplingParams.
- Add GPU setup: Ensure CUDA 12.0+ and 20GB+ VRAM for 7B models.
- If you rely on Groq's ultra-low latency (<150ms), expect 2-3x higher first-token latency on standard GPUs: evaluate if SGLang's throughput and cost savings offset this. Switching from SGLang to Groq is simpler: replace engine.generate() with Groq client API call, but you lose model flexibility and multi-model support.
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