This is the method I actually use, demonstrated end to end on one machine. The rule underneath it: establish the physical ceiling, predict what the software should reach, then change one variable at a time and explain every number — and keep the hypotheses that got refuted, because they save more time than the ones that held.

The machine is Galactus: an AMD EPYC 7713 (64 cores / 128 threads, Zen 3), 2 TB of DDR4-2933 across 8 channels, and 4 × AMD Radeon Pro V620, running llama.cpp and ROCm in a container on Proxmox. The workload is hybrid Mixture-of-Experts inference — routed experts held in system RAM, the dense path (attention, shared experts, KV cache) on the GPUs. The headline model is GLM-5.2 (753 B parameters, 435 GiB at Q4), with DeepSeek-V4-Flash along for the speculative-decode section.

By the end, GLM-5.2 prefill went from 37.6 to 119.4 tokens/second and decode from 5.2 to 7.1 — and, more importantly, both ceilings are explained rather than stumbled into.

Step 1: Start at the wall, not the model

For CPU-resident MoE, decode speed is set by one thing: how fast the active experts can be read out of DRAM every token. So the first measurement isn’t a model benchmark at all. It’s memory bandwidth.

I run STREAM as a thread sweep, then apply the read-for-ownership (RFO) correction. STREAM undercounts write traffic, because an ordinary store first has to read the cache line it’s about to overwrite — traffic STREAM never counts. The fix is to scale the write-heavy kernels: Scale ×1.5, Add and Triad ×4/3. Copy needs no correction if the compiler turned it into non-temporal stores.

There’s a built-in sanity check: uncorrected-Copy plus the RFO correction must not exceed the theoretical ceiling. On Galactus it would have — proof that Copy used non-temporal stores and shouldn’t be corrected. With that resolved, all four kernels converge:

~152 GB/s, which is 81% of the 187.7 GB/s theoretical peak for 8-channel DDR4-2933.

Two things already fall out of this one number. Bandwidth saturates at 16 threads and declines past it — so decode has no reason to want all 64 cores. And 81% of theoretical is a healthy platform; if it had come back at 50%, the story would’ve been “fix your memory topology,” not “tune llama.cpp.”

Step 2: Predict decode before you measure it

Now I use the wall to predict the model, with a two-term model of a decode step:

time_per_token ≈ C + (bytes_read_per_token ÷ bandwidth)

C is a fixed GPU-side cost you measure once. bytes_read_per_token is the active-expert footprint at your quant. For GLM-5.2, C ≈ 90 ms and the experts read ≈ 13.8 GB/token, so:

90 ms + (13.8 GB ÷ 152 GB/s) ≈ 90 + 91 = 181 ms  →  ~5.5 t/s

Measured: 5.53 t/s. I ran the same model against two more configurations (a VRAM-filled “fitter” setup and a CPU-only denominator); it predicted 6.2 and 3.9, and measured 6.01 and 3.87. When the model and the machine agree to within a few percent across three configs, you’re not guessing anymore — you know decode is bandwidth-bound, and you know roughly what any change can buy before you run it. No amount of CPU tuning will move a wall made of DRAM.

Step 3: The iterative loop — one variable at a time

With the ceiling known, the loop is simple to state and easy to botch:

1. Baseline, measured correctly.
2. Change one variable, and sweep it — read the shape, not just the peak.
3. Next variable, carrying forward the previous winner only once you know why it won.

The thread sweep shows why you sweep instead of guessing “more is better”:

Decode peaks around half the physical cores and collapses once you schedule onto SMT siblings. Prefill, meanwhile, keeps climbing with threads — a different curve for the same knob, which is exactly why you don’t reuse a decode-optimal setting for a prefill test.

The war story: how one default poisoned a day of results

Then the microbatch sweep, which is where I got humbled:

That’s a 4× swing on a single knob. And here’s the trap: llama-bench’s -p flag silently clamps n_ubatch to the prompt length. Every “op_offload” prefill number I’d taken that morning had a -p 512 inherited from an old script — so they’d all secretly run at ub 512, not the ub 8192 I thought I was testing. A day of numbers, quietly invalidated by one default. Set -ub explicitly, every single time, and never trust a prefill number until you’ve confirmed the effective microbatch.

Step 4: The dependency tree of changes

The reason that microbatch bug was so costly is that changes aren’t independent — they sit in a tree, and one setting can silently confound everything downstream. Before I trust a result, I place the variable in that tree:

• Some changes poison everything upstream of them. -p clamping n_ubatch is the poster child.
• Some changes only help in one regime. op_offload is a net loss at small microbatch (the per-microbatch streaming cost dominates) and a big win at ub 8192. Test a change where it’s actually able to win.
• Some changes touch only one phase. The scheduler patch below moves prefill and leaves decode untouched — so if decode had regressed, that’d be a bug, not a tradeoff.
• Some paths look equivalent and aren’t. Pinning experts resident on a GPU via -ot placement is a different code path from op_offload, and on GLM-5.2 it measured 17% slower. “Same idea” is not “same performance.”

Draw the tree, and a later measurement can’t be quietly sabotaged by an earlier flag.

Step 5: The capstone — finding a llama.cpp patch by mechanism

This is the whole loop in one finding, and it’s my favorite because a negative result is what cracked it.

Baseline: prefill plateaued at 104.97 t/s (ub 8192). Fast, but I didn’t know if that was the wall or an artifact. So instead of poking flags, I looked at the mechanism — the scheduler’s split histogram:

GGML_SCHED_DEBUG=2 llama-bench -m GLM-5.2-... -ngl 99 -ot "exps=CPU" -fa 1 -v \
  -t 32 -b 512 -ub 512 -p 512 -n 0 -r 1 2>&1 | grep '## SPLIT' | sort | uniq -c

Result: 731 of 1,186 GPU offload splits landed on a single card (ROCm0). Three of four GPUs were sitting idle during prefill. There’s the artifact.

Hypothesis: distribute the offloaded expert matmuls across all four cards.

Pre-registered null: I predicted, in writing, that distribution alone would do nothing — because the expert-weight copies were already asynchronous, and the thing actually serializing them was a per-split synchronize used to read the routing IDs. If that sync is the real bottleneck, spreading the work to four cards just gives you four streams that each still wait their turn. I built distribution-only first specifically to try to refute my own hypothesis: it came back 105.71 vs 104.97 — no change, exactly as predicted. The histogram equalized perfectly while throughput didn’t budge, which is the tell that you’ve moved a mechanism without moving the bottleneck.

The real fix: at prefill-sized batches, essentially every expert is used by some token, so reading the IDs to find “which experts are active” saves no bandwidth — it only imposes the serializing sync. Skip the read at large batch, mark all experts used, and the copies can issue immediately and overlap compute across cards. Three small edits to ggml/src/ggml-backend.cpp:

+13.7%, histogram equalized to 285/300/294/292 across the cards. The full patch and reproduction steps are in the repo. The point for this post isn’t the 13.7% — it’s how it was found: predict, measure the mechanism, and let a refuted hypothesis point at the real cause.

Step 6: Decode gains live in speculation

Prefill was scheduling; decode is physics. The two-term model already told me decode was pinned to the DRAM wall, and no CPU knob — threads, affinity, polling, hugepages, repacked kernels — moved it, because none of them change bytes or bandwidth. The only lever that reads fewer bytes per accepted token is speculative decoding.

• GLM-5.2 gained multi-token-prediction support upstream (--spec-type draft-mtp). Sweeping draft depth: n=1 → 6.8, n=2 → 7.1, n=3 → 6.9 t/s. Locked at n=2: +31%, and past the 7 t/s reading-speed bar for the first time.
• DeepSeek-V4-Flash-0731 with DSpark (--spec-type draft-dspark, a block-5 drafter in VRAM) went further: baseline ~10.1 → 14.7 t/s at n=3, about +45% — the fastest decode this machine has produced on any model.

The depth curves have a shape worth internalizing. Both peak at n=2–3 and regress by n=5: on a top-k-of-many MoE, each drafted token activates a nearly disjoint set of experts, so deep speculation pays more expert-read bytes than its acceptance rate earns back. Same verify-tax the model predicts; you can see it in the numbers.

Step 7: Record everything — including the “no”s

Every run gets logged with its full configuration — date, exact flags, build, metric, value, source line. A tokens/second figure without its flags and build isn’t a result; it’s an anecdote.

And I log the dead ends deliberately. Over this investigation, all of these were measured and refuted for this workload: NUMA imbalance, container overhead, threadpool polling, strict CPU affinity, CPU_REPACK, transparent hugepages, -sm row, pipeline parallelism, ZenDNN, and HIP managed memory. That list is the single most useful thing the whole effort produced, because every “no” is a road the next person doesn’t have to drive down.

The takeaway

The specific numbers here are Galactus’s. The method isn’t:

1. Measure the physical ceiling (STREAM + RFO).
2. Predict the workload from it (the two-term model).
3. Sweep one variable at a time, reading shapes.
4. Place each change in the dependency tree so nothing is confounded.
5. Debug by mechanism, and trust refuted hypotheses.
6. Record results and the “no”s.

That’s the difference between “I changed a flag and it got faster” and “I know what this machine can do, and why.” The terse, copy-pasteable version of this procedure — and every benchmark row behind these numbers — lives in the llm-performance-engineering-notebook repo.