expXa · LLM inference engine

Beyond the
bottleneck.

expXa is an inference engine for large language models. Explore Q6_K quantized execution, native multi-token prediction, and hybrid CPU/GPU mixture-of-experts processing—with measured results on NVIDIA V100 hardware.

Q6_KHIGHER-PRECISION QUANTIZED WEIGHTS
CPU + GPUHYBRID EXPERT EXECUTION
Native MTPMULTI-TOKEN PREDICTION
MeasuredWORKLOAD-SPECIFIC RESULTS
01 / Execution profiles

LLM inference.
Two execution profiles.

The useful question is where an engine spends its time. Kairo and Rust exercise different model geometries, quantization, and execution paths.

PROFILE 01 / KAIRO

Kairo: Q6_K inference.

Qwen3.6 35B-A3B in Q6_K. Kairo’s expXa profile combines tuned attention with native multi-token prediction for a long-context model.

Quantization
Q6_K
Configured context
262,144 tokens
Attention / prediction
4×8 tile / depth-1 MTP
Hardware platform
Dual V100-SXM2 32 GB

Context capacity is a configuration, not a throughput result. The Lab records 65K-input retrieval checks and preserves a failing raw-completion case alongside passing chat variants.

PROFILE 02 / RUST

Rust: hybrid CPU/GPU experts.

Qwen3-30B-A3B in Q4_K_M. Rust’s CPU-heavy mixture-of-experts workload puts scheduling, weight placement, and activation preparation under the microscope.

Quantization
Q4_K_M
Configured context
8,192 tokens
CPU expert banks
40
Generation / batch threads
8 / 20

The matched V100 comparison below preserves weights and placement. Approximately 21% of model weight buffers are on GPU; this is not a measure of GPU compute share.

02 / Benchmark console

expXa vs PXA4.
V100 benchmarks.

expXa versus PXA4 on the same Rust model and V100 platform. Select a prompt length to inspect prefill, generation, and complete request time.

RUST · V100 · NOVEMBER 3, 2026
3 repeats / 128 output tokens / medians
■ expXa 0982 · parallel preparation   ■ PXA4 · tuning control
Observed request-time ranges
PromptexpXa median (range)PXA4 median (range)
512 tokens6.00 s (5.77–6.07)6.95 s (5.91–7.45)
4,096 tokens24.27 s (24.16–27.10)24.38 s (24.01–26.76)
V100 throughput: median tokens per second
Input tokensEnginePrefill tok/sGeneration tok/s
512expXa 0982203.9737.28
512PXA4180.8231.23
4,096expXa 0982201.5332.96
4,096PXA4202.0030.41
Long-prompt prefill and request medians are effectively close; the ranges overlap. These are small shared-host samples, not a universal ranking. Full methods, individual runs & downloadable evidence ↗
01 / WHAT WAS HELD FIXED

Same model. Same placement.

Original weights, token fixtures, 40 CPU expert banks, 8/20 threads, Q8 KV cache and the native streaming client. Small canaries precede scoring; no full-shape warmup.

02 / HARDWARE MATTERS

V100 is not Titan Xp.

The earlier Titan Xp GPU-heavy test favored PXA4: at 4,096 input tokens, 63.51 s versus expXa’s 85.65 s. Hardware, CPU quota, placement and engine revision distinguish that study.

03 / KEEP CLAIMS PRECISE

One chart is not the whole engine.

These Rust numbers do not describe Kairo’s Q6_K path. The Lab’s Kairo before/after checks measure continuity and regression; they should not be relabeled as a matched PXA4 comparison.

03 / Open the evidence

The numbers have
a backstory.

Methods, raw runs, failure cases, and the experiments that come next.

Explore Kairo Lab ↗