38 Signals

Local LLM Quantization Benchmark Lab

A fixed-price testing service that identifies the cheapest local model configuration that still passes a buyer's real workloads.

Added Aug 20, 2026

AI infrastructure

model evaluation

deployment consulting

Opportunity score

Medium opportunity (72%)

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The Problem

Teams deploying local LLMs cannot reliably infer production quality from model size or precision labels. A quantized model may perform well on short prompts but fail unpredictably during coding, tool use, multimodal work, or long-running agent tasks, while hardware compatibility can introduce errors or erase expected speed gains.

Potential Solution

Provide a managed benchmark service that tests a buyer's prompts, evaluation cases, model candidates, quantization formats, and target hardware. The deliverable is a reproducible comparison of quality, latency, memory use, throughput, and failure patterns, followed by a recommended deployment configuration. Start as a hands-on laboratory service and productize the repeated test harness and hardware profiles over time.

Why Now?

Long-horizon agent workflows make small model degradations compound into visible failures. At the same time, rapidly changing model formats and hardware-specific precision support make configuration decisions harder to generalize from public benchmarks.

Market validation

Search demand

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Competition

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Showing 1-20 of 38 signals

RedditSep 12, 2026

r/LocalLLaMA

This draft model is OP on 16 GB cards for Qwen 3.8 27b

huggingface.co/.../Qwen3.8-27B-DFlash2-Q2_K... I used this draft model with huggingface.co/.../Qwen3.8-27B-GSQ-RCO-GGUF with the IQ3_XXS with 128k context and I saw it averaging about 60 tokens per second tg speed on the 16 GB RX 9070 XT. This is much better than using the built in MTP since the built in MTP seems to multiply the required VRAM. For single thread mode, I'd say this is one of the best choices on a 16 GB GPU. Though speculative draft models seem to make it more sensitive to GTT overflow, so if you use more VRAM than you have, then turning off speculative decoding is actually faster, at least on my DDR5 PCIE 5 system. I use kv cache quantization of k 8_0 and v 4_0 for this setup. This person has done more extensive testing on a different card huggingface.co/.../3

RedditSep 11, 2026

r/AI_Agents

RTX 4090 24GB + 32GB RAM - what local model/quant should I run with Hermes?

Hey everyone! I'm getting into Hermes and local LLMs and I'm trying to figure out the sweet spot for my hardware. I'm currently running Hermes on an Ubuntu VM with GPU passthrough. **Specs:** * RTX 4090 - 24GB VRAM * 32GB system RAM * 16 vCPUs * Ubuntu / KVM-QEMU * CPU: INTEL(R) XEON(R) GOLD 5512U (16) @ 2.10 GHz What model size would you realistically recommend for this setup? I'm mainly wondering whether I should target something around **14B, 27B/32B at Q4**, or if trying a larger model with partial CPU/RAM offloading is actually worth it. I'm more interested in **good agentic/tool-use performance** than simply being able to load the biggest possible model. What **model + quantization + context size** are you guys running on similar 24GB GPUs? Also curious about the tokens/sec you're getting on a 4090. Still learning the local LLM side of Hermes, so any tips are appreciated!

RedditSep 10, 2026

r/LocalLLM

Qwen3.8-27B on 23GB L4 for production IASMO + coding?

Hey everyone, I'm working on an enterprise multi-agent ERP assistant and my team is currently evaluating whether we should move from **Qwen3-14B to Qwen3.8-27B**. We also currently use **Claude Code for development**, and my boss is considering moving our coding workflow to Qwen as well, potentially using Qwen3.8-27B as the main model for both development and our production AI system. Our hardware is the part I'm worried about: * 2 × NVIDIA L4 * \~23–24 GB usable VRAM per GPU * Currently using vLLM * The system is a multi-agent ERP assistant with tool calling, RAG, SQL/ERP operations, etc. * We're targeting roughly 100 users * The model can receive fairly large contexts because of agent/tool outputs and retrieved ERP information * We also have other GPU workloads such as OCR/embedding models My main question is: **Is Qwen3.8-27B actually a sensible production choice on 23–24 GB GPUs, or are we going to run into VRAM/context/concurrency problems?** I understand that a Q4 quant can technically fit on a 24 GB card, but I'm more concerned about what happens in a real agentic workload: * KV cache * 16K/32K/64K+ contexts * multiple concurrent requests * tool calls * long agent trajectories * vLLM overhead * batching * latency under load For those who have actually deployed Qwen3.8-27B locally: 1. What quantization are you using? 2. How much VRAM does it actually consume at 8K / 16K / 32K / 64K context? 3. What tokens/sec are you getting on a 24GB GPU? 4. How does it behave with multiple concurrent requests? 5. Is it actually usable for agentic workloads, or does the context/KV cache become the bottleneck? 6. Would you recommend Qwen3.8-27B over Qwen3-14B for production on this hardware? 7. For coding specifically, would you choose **Qwen3.8-27B or Qwen3-Coder-30B-A3B**? 8. Has anyone successfully used either of them as a replacement/backend for Claude Code in a real repository? I'm especially interested in ...

RedditSep 8, 2026

r/LocalLLaMA

best local setup for qwen 3.6 27b/gemma 4 31b on a single 4090?

looking for some advice from anyone running a 24gb card for local dev tasks. i have an rtx 4090 with 64gb ddr5 ram + ryzen 7950x. i'm trying to shift as much inference as possible to my local machine so i can index long codebases and process internal docs without hammering external apis. my current workflow consists of claude code and moclaw for task automation and handling context, which works just fine for my daily work schedule, but i wanna switch to heavy local models for privacy reasons. ive been testing qwen 3.6 27b and gemma 4 31b. qwen 3.6 27b runs pretty smooth at q4 in vram with solid speed, but when i try running bigger dense models or gpt-oss 20b with context offloading the generation speed drops off a cliff. for those running a single 4090 what quantization or backend setup gives you the best balance between context size and tok/s? do you stick with qwen 3.6 27b or is there a trick with vllm or llamacpp offloading to speed up larger contexts without tanking performance? thanks in advance for any tips!

RedditSep 7, 2026

r/LocalLLaMA

Let’s create a new benchmark that actually tells us people here just how good a model is

We already have a thousand ways to measure model quality, and honestly it just makes things harder. There are already too many benchmarks, so I'm not sure we need another one. What seems to be missing in local AI benchmarking is automatic tuning. Nobody is really focusing on finding the best runtime parameters for a given hardware setup, architecture, and quantization. That's a much more practical problem for end users. That's pretty much the gap I'm trying to fill rn

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Local LLM? Quantization Benchmark Lab

The Problem

Potential Solution

Why Now?

Market validation

This draft model is OP on 16 GB cards for Qwen 3.8 27b

RTX 4090 24GB + 32GB RAM - what local model/quant should I run with Hermes?

Qwen3.8-27B on 23GB L4 for production IASMO + coding?

Unlock 35 more signals

Local LLM Quantization Benchmark Lab