PCIe 3.0 · 100 Gb · U.2
FX100 all-flash NVMe-oF array
FX100 has the most complete benchmark record in the FX series: it is the device under test in R1–R5 and R9, across AMD MI308X, MetaX N260 and Huawei Ascend 910B. It offers 100 Gb per port and 16M IOPS, and is sized at one array per 8 inference nodes in production.
Updated
Specification
| Interface generation | PCIe 3.0 |
|---|---|
| Rate per port | 100 Gb |
| IOPS | 16M |
| Flash form factor | U.2 |
| Controllers and expansion | Dual controllers, 2×3 ports (ASIC) |
| Drive configuration | 24 × 7.68 TB Gen4 enterprise U.2 NVMe SSDs |
| Status | In production |
| Reference price | ¥371,200 |
How the FX100 reference price is calculated
| Platform (without drives) | ¥160,000 | |
|---|---|---|
| Enterprise NVMe SSDs | 24 × ¥8,800 | ¥211,200 |
| FX100 fully populated reference price | 184.3 TB · ≈¥2,014/TB | ¥371,200 |
Fully populated reference price = platform ¥160,000 + 24 × ¥8,800 = ¥371,200 for 184.3 TB raw, about ¥2,014 per TB. SSDs are counted at a reference market price that moves with the market; the purchase contract sets the final price.
Who it is for
- Teams that want to prove the benefit of KV cache tiering in a joint test before scaling out
- MI308X or Ascend 910B inference clusters that already run 100 GbE RoCEv2
- Existing clusters held back by NFS, where model loading and switching take too long
Related test reports
| Metric | Value | Baseline | Source |
|---|---|---|---|
| Higher inference throughput | +29–40% | vs. a single local NVMe drive | R2R3 |
| Lower time to first token (TTFT) | −26–32% | vs. a single local NVMe drive | R2 |
| Faster than recomputing without external storage | 8.6–20× | vs. full recompute with no external storage | R2 |
| TTFT gain from the LMCache parallel-read patch | 4.1× | vs. before the patch | R1 |
| Faster model loading | 6.2–9.3× | vs. NFS, on Huawei Ascend 910B | R9 |
| Faster training checkpoint saves | 1.9× | vs. the baseline in report R1 | R1 |
| Effective compute utilization under model switching | 46.7% → 62.8% | at 20 model switches per hour | R1 |
| Concurrent model loading on 8 GPUs | 16% / 30% | vs. the baseline in report R1 | R1 |
| Deviation between two independent runs | ≤5% | same load level, measured twice | R3 |
| HBM residency an external KV tier can stand in for | 128 GB | MetaX N260 single GPU, 14B model, seven-way comparison | R5 |
| Arrays to inference nodes | 1 : 8 | production sizing basis | R1R2R3 |
- R1FX100 Comprehensive LLM Inference and Training Benchmark (8 × AMD MI308X)One report, five questions: how much the LMCache parallel-read patch helps, whether KV tiering scales across GPUs, how much faster 8 GPUs load models concurrently, how effective utilization changes under frequent model switching, and how much faster concurrent training checkpoint writes become. Test models are Qwen2.5-32B and 7B.
8 × AMD Instinct MI308X · ROCm 7.2 - R2FX100 KV Cache Benchmark (480B, TP8, Long Context)Qwen3-Coder-480B-FP8 deployed as one 8-GPU instance (TP=8), the standard production topology, under a long-context cold-recovery load. A three-way comparison of FX100, a single local NVMe drive and full recompute without external storage, stepped across concurrency 8 / 16 / 32. Most throughput and TTFT figures quoted on this site come from this report.
8 × AMD Instinct MI308X · ROCm 7.2 - R3FX100 KV Cache Benchmark Summary (480B, TP4×2, All Metrics)The same 8-GPU server redeployed as two 4-GPU instances (TP4×2), with every metric recorded across nine load levels: TTFT p50 / p90 / p99, TPOT, throughput and disk bandwidth. The report also verifies two things: the fs:// shared pool lets both instances hot-share one cache, and two independent runs at the same level deviate by no more than 5%.
8 × AMD Instinct MI308X · ROCm 7.2 - R4FX100 KV Cache Benchmark (480B, Multi-Instance, No. -006)The official compilation of R2 and R3, issued by a third-party testing organization as report No. -006. If your procurement or compliance process needs a document from an independent body, this is the one to cite.
8 × AMD Instinct MI308X · ROCm 7.2 - R5FX100 KV Cache Benchmark (14B, HBM Efficiency)This report answers whether an external KV tier can stand in for GPU memory. A seven-way comparison on a 14B model (GDS direct read, recompute at two HBM sizes, direct read with low HBM) builds a three-step argument for HBM equivalence. The platform is a single MetaX N260 GPU; the method carries across platforms, but the numbers are never mixed with MI308X results.
Single MetaX N260 GPU - R9FX100 on Huawei Ascend 910B: Inference and Training Benchmark (vs. NFS Baseline)Three comparison groups against an NFS baseline on an Ascend platform with 8 × Huawei Atlas 910B: model loading for inference (DeepSeek-32B / 70B), training weight and checkpoint I/O (Qwen-7B), and training-data acceleration (YOLOv8 / COCO). The platform is Ascend 910B, not MI308X, and is labeled as such wherever this site quotes it.
8 × Huawei Atlas 910B (Kunpeng-920)
Where it is used
- Storage Acceleration: KV Cache Tiering
Storage acceleration
- Enablement for Domestic and Non-NVIDIA Accelerators
Accelerator enablement
- AI Datacenter Efficiency Optimization
Efficiency optimization
- AI Datacenter Construction
Datacenter construction
Other models in the series
Test jointly first, decide afterwards
Test jointly first, decide afterwards
Every engagement starts with a gated joint test of about 10 weeks. Four gates are passed in sequence; if one is missed, the project stops there, so you never pay for a benefit that was not demonstrated.
- Contact
- Wang Limin
- Phone
- +86 131 6181 8898
- mingxin@agentmail.to
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