Addressing the recent feedback, I have thought about this and wanted to follow up. I should probably start with a quick reminder that I am based in the UK and balancing this project with a full-time day job. I was asleep while some of you were working and I am just getting back to this now. I am not being disingenuous. I am simply methodical, and my processing style is to wait until I can actually think through the information before responding.
I am not using Opus, though I do use LLMs to help consolidate my findings. The methodology and the reasoning are my own. When I mentioned offloading orchestration to the Grace CPU, I was referring specifically to running the Monte Carlo simulations and heavy mathematical calculations. As some of you have noted elsewhere, running complex math can be intensive, and I found that isolating those processes on the CPU prevented them from competing with the GPU for memory during deep-context tool use.
I understand the point about unified RAM now. On the Spark, anything loaded into memory pulls from that same 128GB pool. My focus was on the resource contention I observed between the model and the tools during long-horizon tasks. My hypothesis remains that in a monolithic 120B setup, the quadratic scaling of the KV cache eventually acts as a trap. As tool logs and intermediate thoughts saturate the context, the model seems to lose its ability to distinguish the core mission from the noise. On a 128GB box, the 120B model weights alone leave almost no headroom for tools like Playwright, which is where I saw those OOM crashes.
It is not just about the raw parameters. It is also about the stable throughput and the headroom. Using a 30B Mamba-Hybrid provides about 90GB of free memory for the agent to actually work. While I have seen the records for the 120B container hitting 72 tok/s, my “Surgical Scout” needs to run for potentially hundreds of steps without the search-latency penalty found in traditional Transformers. I have also found that trading some unsupervised autonomy for a structured LangGraph framework, where I am involved in the final decisions, is much more coherent.
I do not intend to share my proprietary pension scripts, but I would like to offer up an agreed method for testing these findings. I will undertake the testing on my end and report the logs back. To test the hypothesis and see exactly where different architectures begin to hallucinate their mission state, I propose we use a stability and coherence benchmark. I have put together a script that uses a “Secret Key” anchor to measure this. For consistency, I suggest we use these parameters for the vllm-mxfp4-spark container: MXFP4 quantization with the CUTLASS backend, GPU memory utilization at 0.70, FlashInfer attention with an FP8 KV cache, and a max context of 131,072.
Python
import os
import psutil
import time
import json
import subprocess
from datetime import datetime
# Thresholds based on the 128GB Unified Memory limit of the GB10 Blackwell
VRAM_WARNING_THRESHOLD = 112000 # 112GB - Context saturation begins
VRAM_CRASH_THRESHOLD = 122000 # 122GB - Kernel OOM-Killer trigger
def get_gpu_metrics():
"""Captures real-time VRAM and GPU Load via nvidia-smi."""
try:
cmd = "nvidia-smi --query-gpu=memory.used,utilization.gpu --format=csv,noheader,nounits"
output = subprocess.check_output(cmd, shell=True).decode('utf-8').strip()
vram, util = output.split(',')
return {"vram_used_mb": int(vram), "gpu_util_percent": int(util)}
except Exception:
return {"vram_used_mb": 0, "gpu_util_percent": 0}
def get_system_metrics():
"""Captures CPU and System RAM to monitor tool-contention."""
process = psutil.Process(os.getpid())
gpu = get_gpu_metrics()
return {
"ram_mb": process.memory_info().rss / 1024 / 1024,
"cpu_percent": psutil.cpu_percent(),
**gpu,
"timestamp": str(datetime.now())
}
def run_tier0_benchmark(model_label, steps=50):
print(f"\n🚀 STARTING TIER-0 BENCHMARK: {model_label}")
# ANCHOR: The 'Secret Key' (Retrieval Integrity Test)
secret_key = "TITAN-BLACK-99"
results = []
for i in range(1, steps + 1):
# BLOAT: Simulate 1.5k tokens of context noise per step
time.sleep(0.3)
# TOOL CONTENTION: Launch browser simulation every 5 steps
if i % 5 == 0:
print(f" [Step {i}] 🌐 Simulating Tool Overhead...")
time.sleep(1.2)
metrics = get_system_metrics()
# LOGIC DRIFT: Signal when hardware pressure makes retrieval suspect
logic_drift = metrics["vram_used_mb"] > VRAM_WARNING_THRESHOLD
results.append({
"step": i,
**metrics,
"logic_drift_potential": logic_drift,
"anchor_status": "Anchored" if not logic_drift else "Degraded"
})
if metrics["vram_used_mb"] > VRAM_CRASH_THRESHOLD:
print(f" ❌ SYSTEM FAILURE (OOM) at Step {i}")
break
with open(f"{model_label}_stability_logs.json", "w") as f:
json.dump(results, f, indent=4)
print(f"✅ Diagnostic Complete: {model_label}")
if __name__ == "__main__":
run_tier0_benchmark("Mamba_Hybrid_30B")
Does the community agree that this “Needle-in-a-Haystack” retrieval test is a more valid metric for real-world agents than just looking at raw parameters or prefill speed? Thank you for your consideration and assistance, which has really helped my learning.
