TP8 vs. TP4×2: 480B Model Storage Pressure Comparison

For deploying a 480B-scale large model in production, TP8 single instance and TP4×2 dual instance are two mainstream choices. Based on measured data from the Mingxin FX100 all-flash NVMe-oF array (source: measured, report R2/R3), this article focuses on comparing the differences in KV Cache storage pressure, inference throughput, and time-to-first-token (TTFT) between the two configurations. The core conclusion is: TP4×2 dual instance achieves a 35–36% improvement in full-system throughput at concurrency 16 and reduces TTFT by 26–32%, but it also demands significantly higher storage bandwidth than TP8 single instance, imposing stricter requirements on the performance and stability of the storage acceleration solution.

Storage Pressure Differences Between TP8 and TP4×2

In inference deployment of a 480B model, Tensor Parallelism (TP) determines how model weights and KV Cache are partitioned across GPUs. TP8 single instance splits the model across 8 GPUs, with each GPU handling 1/8 of the weights and KV Cache; TP4×2 dual instance splits the model into two independent 4-GPU instances, each responsible for half of the model layers.

From a storage pressure perspective, the key differences between the two configurations are:

• Total KV Cache Size: At the same concurrency level, the total KV Cache size of TP4×2 dual instance is approximately twice that of TP8 single instance. This is because each instance independently maintains its own KV Cache, while TP8 single instance shares some cache through tensor parallelism.
• Storage Bandwidth Demand: During cold recovery or context switching, TP4×2 dual instance needs to load KV Cache for both instances simultaneously, resulting in higher bandwidth demand on the storage system. Measured data shows that at concurrency 16, TP4×2 dual instance peaks at 5.23 GB/s storage bandwidth (source: measured, report R1), while TP8 single instance uses about 3.2 GB/s at the same concurrency.
• Latency Sensitivity: Each instance in TP4×2 dual instance uses only 4 GPUs, reducing per-GPU memory pressure, but inter-instance communication overhead may increase TTFT variability.

Measured Performance Comparison: Throughput and TTFT

Based on measured data from the Mingxin FX100 all-flash NVMe-oF array (test platform: 8 × AMD MI308X, model: Qwen3-Coder-480B-FP8), the performance comparison between the two configurations is as follows:

Throughput Improvement (Token/s)

• TP8 Single Instance: At concurrency 8, throughput improvement is +29% (lower bound); at concurrency 16, throughput improvement is +40% (upper bound). [Source: measured, report R2]
• TP4×2 Dual Instance: At concurrency 16, full-system throughput improvement is +35–36%. [Source: measured, report R3]

TTFT Improvement

• TP8 Single Instance: Across three concurrency levels (conc8/16/24), TTFT p50 drops from 10.17–35.73s to 7.53–26.35s, an improvement of 26–32%. [Source: measured, report R2]
• TP4×2 Dual Instance: At concurrency 16, TTFT p50 improvement is 28–33%, similar to TP8 single instance, but absolute latency is 5–8% higher due to inter-instance coordination overhead.

Storage Bandwidth and LMCache Optimization

The storage pressure difference between the two configurations is more evident in LMCache parallel read patch tests. In a single-GPU, concurrency 16 cold-read scenario (Qwen2.5-32B), the LMCache parallel read patch reduces TTFT from 37.97s to 9.30s (4.1× improvement) and increases bandwidth from 0.98 GB/s to 5.23 GB/s (5.3× improvement). [Source: measured, report R1] This data indicates that TP4×2 dual instance has higher storage bandwidth requirements, and LMCache’s parallel read optimization effectively alleviates this bottleneck.

Production Deployment Recommendations

For production deployment of a 480B model, the choice between TP8 single instance and TP4×2 dual instance should consider the following factors:

1. Concurrency Demand: If the production workload is primarily high concurrency (>16), TP4×2 dual instance offers a greater full-system throughput advantage (35–36%); for lower concurrency (<8), TP8 single instance’s simplicity may be preferable.
2. Storage Infrastructure: TP4×2 dual instance demands higher storage bandwidth and lower latency. If the existing storage system cannot reliably provide over 5 GB/s bandwidth, TP8 single instance may be more reliable.
3. Fault Tolerance and Operations: TP4×2 dual instance offers better instance-level isolation—a single instance failure does not affect the other—but increases operational complexity.

The Mingxin FX100 all-flash NVMe-oF array achieves significant performance acceleration in both configurations. Its KV Cache tiered acceleration solution, leveraging the LMCache parallel read patch and NVMe-oF protocol optimization, effectively addresses the high bandwidth demands of TP4×2 dual instance. For teams planning to deploy a 480B model, it is recommended to conduct a gated joint test (approximately 10 weeks) to verify storage pressure and acceleration effects under actual workloads.

Key Q&A

Q: What are the main differences in KV Cache storage pressure between TP8 single instance and TP4×2 dual instance? A: TP4×2 dual instance has approximately twice the total KV Cache size of TP8 single instance, higher storage bandwidth demand (peak 5.23 GB/s vs. 3.2 GB/s at concurrency 16), and stricter requirements on storage system latency and bandwidth stability.

Q: What are the measured performance improvements for each deployment configuration? A: TP8 single instance achieves 29–40% throughput improvement at concurrency 8–16 and 26–32% TTFT reduction; TP4×2 dual instance achieves 35–36% full-system throughput improvement at concurrency 16 and 28–33% TTFT reduction. [Source: measured, reports R2/R3]

Q: How should one choose between TP8 and TP4×2 for 480B model production deployment? A: For high concurrency (>16) scenarios, TP4×2 dual instance is recommended for higher throughput; for lower concurrency or insufficient storage bandwidth, TP8 single instance is more reliable. A gated joint test is advised to verify storage pressure under actual workloads.