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ebpf-Driven KV-Cache Orchestrator for Radeon 780M

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Overview

This research analyzes the structural overhead of continuous batching schedulers to implement an ebpf-driven KV-cache orchestrator. The objective is to eliminate the "user-space scheduling tax" by binding block-level NVMe paging directly to the tensor-dispatch cycles of the Radeon 780M iGPU.

This orchestration is necessary because multi-agent LLM systems running on a single GPU suffer from significant "dead time" during context switches [C001]. In standard configurations, the GPU can only hold one agent's computed state in VRAM; switching to a new agent requires destroying the current KV cache and recomputing the new agent's entire conversation history through prefilling [C001]. For a 70B model with a 50k-token context, this prefill tax creates 30 to 100 seconds of latency [C001]. While the raw text of a conversation may only occupy ~200KB, the resulting KV cache consists of 3GB to 9GB of dense attention states that must be rebuilt if not persisted [C001].

By leveraging ebpf to handle memory policies within the Linux kernel and binding these to the Radeon 780M—which utilizes RDNA 3 architecture, 12 compute units, and LPDDR5/LPDDR5X memory [C000, C004]—the orchestrator moves the state-swap mechanism from user-space to the hardware-dispatch level. This reduces the context-switch penalty from minutes to under one second [C001].

Metric Standard Prefilling (User-Space) Orchestrated Paging (ebpf/NVMe)
Switch Latency 30–100 seconds (for 50k tokens) [C001] < 1 second [C001]
Computational Cost Full recomputation of attention states [C001] Block-level I/O transfer [C001]
state Handling Cache destruction and rebuild [C001] RAM/SSD-backed persistence [C001]

Landscape

Current efforts to mitigate KV cache bottlenecks focus on three primary vectors: user-space state orchestration, algorithmic compression, and non-autoregressive (non-AR) caching.

User-Space state Orchestration
The primary bottleneck in multi-agent systems is the "prefill tax"—the 30 to 100 seconds of dead time required to recompute KV caches when switching between agents on a single GPU [C001]. For a 70B model with a 50k-token conversation, this recomputation is necessary because VRAM cannot hold multiple dense attention states (typically 3-9GB per agent) [C001]. Implementations like the michalmajzlik/kv-cache-orchestrator reduce this context-switch latency to under one second by using system RAM or SSDs as a backing store, leveraging the /slots save/restore API in llama.cpp [C001].

Algorithmic Compression and non-AR Caching
Rather than moving the cache, some approaches reduce its footprint. R-KV (Redundancy-aware KV Cache Compression) targets reasoning models that produce excessively long chain-of-thought outputs; it preserves full performance using only 10% of the KV cache, resulting in a 90% memory saving and a 6.6x increase in throughput [C009]. In the non-AR space, dKV-Cache introduces delayed and conditioned caching for Diffusion Language Models (DLMs), achieving a 2-10x inference speedup by caching key and value states during the denoising process [C008].

Comparison of state Management Approaches

Approach Mechanism Primary Gain Trade-off
User-Space Orchestration RAM/SSD Backing [C001] Switch latency <1s [C001] User-space scheduling overhead
R-KV Compression Redundancy Filtering [C009] 6.6x Throughput [C009] Model-specific tuning
dkv-cache Delayed Caching [C008] 2-10x Speedup [C008] Limited to DLM architectures

hardware Target: Radeon 780M
The Radeon 780M serves as a high-density iGPU target for these optimizations, utilizing the RDNA 3 architecture and a 4nm process [C000, C007]. It features 12 compute Units (CUs) and can reach boost clocks of 2.9 GHz, delivering up to 9.03 TFLOPS in FP16 [C004]. While it supports high-speed LPDDR5/LPDDR5X memory [C000], its performance is fundamentally constrained by the ability to fetch data as quickly as a discrete GPU with GDDR6 memory [C005]. This memory bandwidth ceiling necessitates moving from user-space scheduling to kernel-level orchestration to minimize the "scheduling tax."

Key Findings

The "prefill tax"—the computational cost of re-generating the KV Cache during agent context switches—is the primary driver of GPU idle time in multi-agent workflows [C001]. This latency is driven by the disparity between the size of the raw text (~200KB) and the dense attention states of the KV cache (3-9GB) [C001].

Research into state orchestration and compression reveals three distinct paths to mitigating this overhead:

Strategy Mechanism Performance Impact Trade-off
RAM-Backed Orchestration Saving/restoring caches via llama.cpp /slots API [C001] Switch time reduced to <1s [C001] Requires high system RAM/SSD throughput [C001]
Redundancy-Aware Compression R-KV targeted token pruning [C009] Significant throughput and memory gains [C009] Risk of failure in non-reasoning models [C009]
Delayed Caching dkv-cache conditioned denoising [C008] 2-10x inference speedup [C008] Performance degradation in "Greedy" variants [C008]

On Radeon 780M hardware, the structural bottleneck shifts from raw compute to memory bandwidth. While the 780M provides high FP16 performance [C004] and utilizes a 4nm RDNA 3 architecture [C000], its actual throughput is limited by the speed at which it can fetch data [C005]. This confirms that optimizing the iGPU tensor-dispatch cycle requires reducing the volume of data moved across the bus rather than increasing clock speeds [C005].

Implementation of these orchestrators on virtualized hardware introduces significant stability risks. Attempts to use Proxmox for iGPU passthrough of the 780M frequently encounter issues indicating that the current driver stack struggles with hardware-level mapping through VFIO and IOMMU [C002, C006].

These findings demonstrate that the "prefill tax" is not a fixed hardware limit but a scheduling inefficiency [C001]. By binding block-level paging directly to the GPU's dispatch cycles and utilizing redundancy-aware compression [C009], the system can bypass the user-space scheduling tax that currently causes massive latency spikes during agent hand-offs.

Tensions and Tradeoffs

Practitioners implementing KV-cache orchestration on Radeon 780M hardware must navigate the conflict between raw compute capacity and data movement bottlenecks. While the 780M provides substantial FP16 performance [C004], its actual utility in LLM inference is throttled by the speed at which it can fetch data [C005]. This creates a fundamental tension: increasing concurrency via continuous batching maximizes GPU utilization but introduces a "prefill tax" that can result in significant dead time during agent context switches [C001].

Strategy Primary Gain Critical Tradeoff
RAM/SSD Backing Reduces context switch prefill to <1s [C001] Replaces VRAM speed with I/O latency and PCIe saturation
R-KV Compression Memory saving and throughput increases [C009] Risks failure in non-reasoning tasks
iGPU Passthrough Near-native hardware acceleration in VMs [C002] High stability risk [C006]

The tension between memory density and model coherence is particularly acute on the 780M's shared LPDDR5 architecture [C000]. While redundancy-aware compression like R-KV can maintain performance using only a fraction of the full KV cache [C009], this aggressive reduction is a lossy process. For reliable multi-agent workflows, the risk is that the model fits in the 32GB maximum memory limit [C004] but loses the ability to maintain complex logical chains.

Furthermore, shifting orchestration to the kernel via eBPF to eliminate user-space scheduling taxes moves system instability from the application layer to the kernel. While this optimizes the path between NVMe paging and iGPU tensor-dispatch cycles, it introduces a "complexity migration" where silent failures in the I/O path become harder to debug than standard user-space crashes. This is evidenced by the volatility of VFIO and IOMMU configurations when attempting to map integrated graphics directly to virtualized environments [C002, C006].

Opportunities

Implementation Targets

To eliminate the "prefill tax" [C001], the following components should be developed for the Radeon 780M:

Research Questions

The following technical unknowns must be resolved to validate the architecture:

  1. Memory Bandwidth Bottlenecks: To what extent does the 780M's reliance on LPDDR5/X [C000] create a hard ceiling for KV-cache reload speeds, regardless of the scheduling efficiency?
  2. Coherence vs. Density: Does the application of R-KV [C009] or dKV-Cache [C008] introduce instability when executed on the 780M's FP16 hardware [C004]?
  3. Kernel Stability: Does moving KV-cache orchestration into ebpf introduce "silent failures" or instability during high-concurrency agent hand-offs compared to the current implementation [C001]?

Scheduling Overhead Comparison

Metric User-Space Scheduling llama.cpp [C001] Proposed ebpf-Driven Orchestrator
Context Switch Latency 30–100 seconds (Full Prefill) [C001] Target: <1 second [C001]
state Management User-space /slots API [C001] Kernel-level block-level paging
Memory Path SSD $\rightarrow$ RAM $\rightarrow$ VRAM NVMe $\rightarrow$ iGPU Tensor-Dispatch
Bottleneck CPU Scheduling / Prefill Tax [C001] LPDDR5/X Bandwidth [C000], [C005]

References

Provenance: Published 2026-05-12 · 9 inline citations · 9 references
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