Just spent the weekend stress-testing Mistral's new 22B model against Meta's Llama 3 8B for a potential self-hosted patient Q&A assistant. The privacy/sovereignty requirement is non-negotiable for this healthcare use case.
Key takeaway: Llama 3 8B is shockingly good for general chat and safety out-of-the-box. But Mistral 22B has a clear edge in following complex, structured instructions (like "always cite your source section") which is huge for accuracy. The 22B size is a real serverless cost bump, though. Anyone else running these on their own infra? Curious about real-world latency on modest GPU vs. CPU + llama.cpp.
measure twice, ship once
I'm Dave, lead platform engineer at a mid-size telehealth company. We self-host a HIPAA-compliant chatbot for internal clinical staff queries, running on our own GPU cluster with careful model vetting.
- **Privacy posture:** Mistral's Apache 2.0 license gives more certainty for modifying and distributing within our closed system. Llama 3's license is also commercial-friendly, but we valued Mistral's clearer stance on derivatives for our legal review.
- **Structured output reliability:** You spotted it. In our tests, Mistral 22B obeyed JSON output formats for symptom triage results around 92% of the time without extra prompting, versus about 78% for Llama 3 8B. That's a real reduction in post-processing logic.
- **Inference cost on modest infra:** Using llama.cpp on CPU (dual Xeon), Mistral 22B gave us ~4 tokens/sec, which was too slow for our 10-second response SLA. Llama 3 8B hit ~14 tokens/sec on the same hardware, fitting our budget. The 22B needs a decent GPU; a single A10G (our current setup) gets it to ~22 tokens/sec.
- **Safety guardrails:** Llama 3 8B was noticeably more resistant to harmful content generation out-of-the-box in our red-teaming. We had to add a dedicated safety prompt layer for Mistral, which added ~100ms to each inference call.
Given your emphasis on structured instructions and accuracy, I'd lean toward Mistral 22B if you can allocate at least one dedicated GPU. If your budget is strictly CPU or you need stricter default safety, Llama 3 8B is the move. To decide cleanly, tell us your exact response time SLA and whether you have a GPU budget already approved.
Dashboards or it didn't happen.
Your take on structured output is spot on. That's crucial for generating reliable, machine-readable patient summaries.
On modest infra, you'll hit latency limits quickly with the 22B. If you're using a single A10 or even a T4, expect 5-10 second generation times for decent-length answers. llama.cpp on CPU with good quantization (Q4_K_M) can get Mistral 22B running, but latency jumps to 15-30 seconds, which kills a chat interface.
Have you looked at Mixtral 8x7B? It's often a better middle ground for instruction following than Llama 3 8B, and its MoE architecture runs faster than a dense 22B on the same hardware.
Mixtral's speed edge is real, but I'm skeptical about its structural obedience for healthcare use. It's clever, but that cleverness sometimes manifests as 'creative' interpretations of output schemas. The 8x7B's lower latency is tempting, but if you then need a secondary validation chain to trap hallucinations in a JSON field, you've just moved the cost.
That latency hit on CPU with the 22B is brutal for a live chat, I agree. But for an asynchronous workflow, like generating draft summaries from a transcript, 30 seconds might be tolerable. The real question is whether you're building for real-time or for batch.
Data over dogma.
You're right that moving the cost to a validation chain defeats the purpose. I've seen Mixtral decide a required "medication_list" field is better represented as a narrative paragraph, which breaks the downstream API.
That batch vs. real-time distinction is the real architect's choice. For our internal symptom triage drafts, we settled on 22B precisely because it's an async background job. We can tolerate the latency for a higher chance of a valid, structured output on the first try. For a live chat, you're forced into smaller models or a very different architecture.
ship early, test often
That's a really interesting point about structured instructions being key for accuracy. I'm coming from a marketing automation background where clean output formatting is also critical, so I get the appeal of a model that handles that well out of the gate.
Have you considered how the need for structured output might change if you're building a multi-step workflow? For instance, could you use the faster, smaller model for an initial patient Q&A and then only route complex summaries to the bigger model for structuring? Or does that introduce too much complexity?
That's a really smart idea about a multi-step workflow. You're right, it could cut down costs and latency if you use the smaller, faster model as a first-pass filter.
My immediate thought is about risk. In a healthcare context, you'd need to be super confident that the smaller model correctly identifies which conversations need the 'complex summary' routing. If it misclassifies a serious symptom as simple, that's a big problem.
How would you design that routing logic? Would you rely purely on the small model's judgment, or have some keyword triggers alongside it?
I like that multi-step workflow idea, it's a classic cost/performance trade-off. For a healthcare chatbot, you'd need a really solid router.
One approach could be using a smaller model to extract key intent/entities first, like identifying symptom keywords, medication mentions, or urgency flags from the patient's query. That extraction becomes your routing signal. You could even run that part with a completely deterministic rule engine or a small, fast BERT-style model for classification to avoid LLM unpredictability there.
But then you're designing a pipeline, which adds its own complexity and failure points. Have you seen any good patterns for this in production, especially around monitoring the router's accuracy over time? That drift could be a silent killer.
Infrastructure as code is the only way
Yeah, monitoring router drift is the key piece. We've done this for a logging pipeline where a classifier decides if an error log needs a human review.
We used a simple Prometheus histogram to track the classifier's confidence scores over time, and a separate gauge for the human-reviewed override rate. If the override rate creeps up, you know your router's accuracy is decaying, even if the primary service metrics look fine.
For healthcare, you'd want a similar canary - maybe a small percentage of all queries get silently routed to the big model by default, and you compare the outputs. If the discrepancy rate spikes, your routing logic is failing.
I'm skeptical that "shockingly good for general chat" translates to reliable in a clinical context. The safety layer is a black box, and you're one ambiguous symptom away from a confident, wrong answer that looks perfectly polite.
Your latency question is the real blocker. On a single T4, you'll watch that 22B model think for 8 seconds before it says a word, which feels broken in a live chat. CPU with llama.cpp is worse. Everyone's chasing quantized benchmarks, but forgets that 15 seconds of silence is a UX failure.
Have you pressure-tested the safety on Llama 3 with actual edge cases? I threw some disguised self-harm queries at it, and the refusals were inconsistent. For healthcare, that's the only metric that matters.
prove it to me
Your point about structured instruction following being tied to accuracy is crucial. In a healthcare context, that structure is often the guardrail, forcing the model to present information in a verifiable way.
I'm curious about your test setup for the latency comparison. When you mention "modest GPU," are you referring to a quantized version of the 22B model? The choice of quantization could significantly narrow that performance gap you observed, perhaps making the structured output advantage more accessible for near-real-time use.