RTX 3090 at 170 tok/s: Complete Technical Configuration Reference

This is the technical companion to the full tuning diary: How I Pushed an RTX 3090 to 170 Tokens Per Second With Qwen 3.6-35B. The Medium article covers the narrative and crash analysis. This page is the copy-pasteable reference — every flag, config file, and benchmark number.

Hardware Specification

Software Stack

llama.cpp Build Command

Architecture-specific build for Ampere (compute 8.6). These flags produce native RTX 3090 machine code and enable all flash attention quantization paths:

cmake -B build \
  -DGGML_CUDA=ON \
  -DCMAKE_CUDA_ARCHITECTURES=86 \
  -DGGML_CUDA_FA_ALL_QUANTS=ON \
  -DGGML_NATIVE=ON \
  -DGGML_LTO=ON \
  -DGGML_CUDA_GRAPHS=ON \
  -DGGML_CUDA_F16=ON

cmake --build build --config Release -j 10

Critical Build Flags Explained

Server Launch Command

The complete, battle-tested launch command:

llama-server --port ${PORT} \
  --model Qwen3.6-35B-A3B-UD-IQ4_XS.gguf \
  --mmproj mmproj-F16.gguf \
  -c 131072 -ngl 99 -fa on -b 1024 -ub 2048 \
  -t 10 -tb 16 \
  --cache-type-k q8_0 --cache-type-v turbo4 \
  -fit off --no-mmap --mlock --jinja \
  --poll 100 --prio 2 --reasoning off \
  -np 1 \
  --spec-type draft-mtp --spec-draft-n-max 2

Flag-by-Flag Reference

MTP Speculative Decoding Benchmarks

MTP (Multi-Token Prediction) uses a lightweight draft model to predict the next 1-2 tokens. The main model verifies them in a single forward pass.

Key insight: MTP is highly effective for predictable content (code, JSON, tool calls) but net-negative for creative writing and short responses. The draft model setup overhead exceeds savings on short outputs.

Why n-gram Stacking Fails

Stacking n-gram speculative decoding alongside MTP was tested. The result: acceptance went up (62%→85% on creative) but speed went down (125→67 tok/s). Two competing draft engines generate redundant drafts that waste verification compute.

One good speculative engine beats two competing ones.

Sampling Parameters (Agent Workloads)

{
  "temperature": 0.6,
  "top_p": 0.95,
  "top_k": 20,
  "repetition_penalty": 1.05
}

Quantization Comparison

IQ4_XS from Unsloth's dynamic quantization gives the best speed-to-quality ratio for 24GB cards. The quality difference vs Q5_K_M is measurable on perplexity but invisible in real agent workloads.

Benchmark Comparison: Published Results

Every documented benchmark we could find for this hardware + model combination:

llama-swap Configuration

llama-swap routes requests to the correct model profile. This is the config structure for a 4-profile setup:

# /home/game/.hermes/scripts/llama-swap-config.yaml
models:
  default:
    cmd: |
      llama-server --port ${PORT} \
        --model Qwen3.6-35B-A3B-UD-IQ4_XS.gguf \
        --mmproj mmproj-F16.gguf \
        -c 65536 -ngl 99 -fa on -b 1024 -ub 2048 \
        -t 10 --cache-type-k q8_0 --cache-type-v turbo4 \
        -fit off --no-mmap --mlock --jinja \
        --poll 100 --prio 2 --reasoning off -np 1 \
        --spec-type draft-mtp --spec-draft-n-max 2

  large-context:
    cmd: |
      llama-server --port ${PORT} \
        --model Qwen3.6-35B-A3B-UD-IQ4_XS.gguf \
        --mmproj mmproj-F16.gguf \
        -c 131072 -ngl 99 -fa on -b 1024 -ub 2048 \
        -t 10 --cache-type-k q8_0 --cache-type-v turbo4 \
        -fit off --no-mmap --jinja \
        --poll 100 --prio 2 --reasoning off -np 1 \
        --spec-type draft-mtp --spec-draft-n-max 2

  # Add additional profiles for other models as needed

OOM Crash Analysis & Fix

What Happens at 131K Context + --mlock on 24GB RAM

The kernel OOM killer selected llama-server (highest RSS). Three interacting causes:

1. 131K KV cache: q8_0 K cache + turbo4 V cache at 131,072 tokens = 4-6 GB on top of model weights.
2. --mlock pinned pages: Forces all model pages into resident RAM. Cannot be evicted.
3. VRAM spillover: Some tensors spill to system RAM. With --mlock, those are pinned too.

Math: 17-18 GB model weights + 4-6 GB KV cache + 1-2 GB overhead = 24+ GB. Zero headroom.

The Fix

Agent Framework Context Optimization

Token savings per turn by optimizing the agent framework config:

Hardware Tier Guide (July 2026 Prices)

RAM recommendation: Whatever GPU you choose, get at least 48GB system RAM. 24GB system RAM is not enough for 131K context with --mlock.

Remaining Headroom: The Last 10%

All benchmarks measured July 25-26, 2026 · llama.cpp b9971 · Qwen3.6-35B-A3B IQ4_XS · RTX 3090 · CUDA 12.9 · Hermes Agent v0.19.0